Front visualization

The jmetal.lab.visualization submodule contains several classes useful for plotting solutions. jMetalPy includes three types of visualization charts: static, interactive and streaming.

Static plots

• It is possible to visualize the final front approximation by using the Plot class:

  from jmetal.lab.visualization import Plot
  
  plot_front = Plot(title='Pareto front approximation', axis_labels=['x', 'y'])
  plot_front.plot(front, label='NSGAII-ZDT1')
  

  Note

  Static charts can be shown in the screen or stored in a file by setting the filename.

  For problems with two and three objectives, the figure produced is a scatter plot; for problems with more than three objectives, a parallel coordinates plot is used. Note that any arbitrary number of fronts can be plotted for comparison purposes:

  plot_front = Plot(title='Pareto front approximation', axis_labels=['x', 'y'])
  plot_front.plot([front1, front2], label=['zdt1', 'zdt2'], filename='output', format='eps')
  

API

class jmetal.lab.visualization.plotting.Plot(title: str = 'Pareto front approximation', reference_front: List[S] = None, reference_point: list = None, axis_labels: list = None)

Bases: object

static get_points(solutions: List[S]) → Tuple[pandas.core.frame.DataFrame, int]

Get points for each solution of the front.

Parameters

solutions – List of solutions.

Returns

Pandas dataframe with one column for each objective and one row for each solution.

pcoords(fronts: List[list], normalize: bool = False, filename: str = None, format: str = 'eps')

Plot any arbitrary number of fronts in parallel coordinates.

Parameters

• fronts – List of fronts (containing solutions).

• filename – Output filename.

plot(front, label='', normalize: bool = False, filename: str = None, format: str = 'eps')

Plot any arbitrary number of fronts in 2D, 3D or p-coords.

Parameters

• front – Pareto front or a list of them.

• label – Pareto front title or a list of them.

• normalize – If True, normalize data (for p-coords).

• filename – Output filename.

• format – Output file format.

three_dim(fronts: List[list], labels: List[str] = None, filename: str = None, format: str = 'eps')

Plot any arbitrary number of fronts in 3D.

Parameters

• fronts – List of fronts (containing solutions).

• labels – List of fronts title (if any).

• filename – Output filename.

two_dim(fronts: List[list], labels: List[str] = None, filename: str = None, format: str = 'eps')

Plot any arbitrary number of fronts in 2D.

Parameters

• fronts – List of fronts (containing solutions).

• labels – List of fronts title (if any).

• filename – Output filename.

Interactive plots

• This kind of plots are interactive in such a way that every solution can be manipulated (e.g., actions such as zoom, selecting part of the graph, or clicking in a point to see its objective values are allowed).

  plot_front = InteractivePlot(title='Pareto front approximation')
  plot_front.plot(front, label='NSGAII-ZDT1', filename='NSGAII-ZDT1-interactive')
  

API

class jmetal.lab.visualization.interactive.InteractivePlot(title: str = 'Pareto front approximation', reference_front: List[S] = None, reference_point: list = None, axis_labels: list = None)

Bases: jmetal.lab.visualization.plotting.Plot

export_to_div(filename=None, include_plotlyjs: bool = False) → str

Export as a div for embedding the graph in an HTML file.

Parameters

• filename – Output file name (if desired, default to None).

• include_plotlyjs – If True, include plot.ly JS script (default to False).

Returns

Script as string.

export_to_html(filename: str) → str

Export the graph to an interactive HTML (solutions can be selected to show some metadata).

Parameters

filename – Output file name.

Returns

Script as string.

plot(front, label=None, normalize: bool = False, filename: str = None, format: str = 'HTML')

Plot a front of solutions (2D, 3D or parallel coordinates).

Parameters

• front – List of solutions.

• label – Front name.

• normalize – Normalize the input front between 0 and 1 (for problems with more than 3 objectives).

• filename – Output filename.

Streaming plots

• The visualizer observer displays the front in real-time (note it only works for problems with two and three objectives) during the execution of multi-objective algorithms; this can be useful to observe the evolution of the Pareto front approximation:

  from jmetal.util.observer import VisualizerObserver
  
  algorithm.observable.register(observer=VisualizerObserver(reference_front=problem.reference_front))
  

API

class jmetal.lab.visualization.streaming.StreamingPlot(plot_title: str = 'Pareto front approximation', reference_front: List[S] = None, reference_point: list = None, axis_labels: list = None)

Bases: object

create_layout(dimension: int) → None

plot(front)

update(front: List[S], reference_point: list = None) → None

Chord plot

API

jmetal.lab.visualization.chord_plot.chord_diagram(solutions: List[jmetal.core.solution.FloatSolution], nbins='auto', ax=None, obj_labels=None, prop_labels={'fontsize': 13, 'ha': 'center', 'va': 'center'}, pad=6)

jmetal.lab.visualization.chord_plot.draw_chord(start_angle1=0, end_angle1=60, start_angle2=180, end_angle2=240, radius=1.0, chord_width=0.7, ax=None, color=(1, 0, 0), z_order=1)

jmetal.lab.visualization.chord_plot.draw_sector(start_angle=0, end_angle=60, radius=1.0, width=0.2, lw=2, ls='-', ax=None, fc=(1, 0, 0), ec=(0, 0, 0), z_order=1)

jmetal.lab.visualization.chord_plot.hover_over_bin(event, handle_tickers, handle_plots, colors, fig)

jmetal.lab.visualization.chord_plot.polar_to_cartesian(r, theta)

Posterior plot

API

jmetal.lab.visualization.posterior.plot_posterior(sample, higher_is_better: bool = False, min_points_per_hexbin: int = 2, alg_names: list = None, filename: str = 'posterior.eps')

Plots the sample from posterior distribution of a Bayesian statistical test. Parameters: ———– data: An (n x 3) array or DataFrame contaning the probabilities. alg_names: array of strings. Default np.array([‘Alg1’, ‘Alg2’])

Names of the algorithms under evaluation

Figure