mooplot.plot_pf#

mooplot.plot_pf(datasets, type='points', filter_dominated=True, **layout_kwargs)[source]#

Plot Pareto fronts. It can produce an interactive point graph, stair step graph or 3D surface graph. It accepts 2 or 3 objectives.

Parameters:

datasets (numpy array) – The dataset argument must be Numpy array produced by the moocore.read_datasets() function, i.e., an array with 3-4 columns including the objective data and set numbers.
type (str, optional) –
Type of plot. Any of:
- ’points’ : produces a scatter-like point graph (2 or 3 objectives)
- ’lines’ : produces a stepped line graph (2 objectives only)
- ’points,lines’ : produces a stepped line graph with points (2 objective only)
- ’fill’ : produces a stepped line graph with filled areas between lines - see plot_eaf() (2 objective only)
- ’surface’ : produces a smoothed 3d surface (3 objective only)
- ’surface,points’ : produces a smoothed 3d surface with datapoints plotted (3 objective only)
- ’cube’ : produces a discrete cube surface (3 objective only)
Abbreviations such as ‘p’ or ‘p,l’ are accepted.
filter_dominated (bool, optional) – Whether to automatically filter dominated points within each set. Default is True.
layout_kwargs (dict) – Update features of the graph such as title axis titles, colours etc. These additional parameters are passed to plotly update_layout. See here for all the layout features that can be accessed: Layout Plotly reference

Returns:

plotly.graph_objects.Figure – The function returns a Plotly GO figure object: Plotly Figure reference

This means that the user can customise any part of the graph after it is created

Examples

>>> x = moocore.read_datasets(moocore.get_dataset_path("input1.dat"))
>>> mooplot.plot_pf(x, type="points,lines")  
Figure({...

Examples using `mooplot.plot_pf`#