Plotting Metrics Across Histories¶

This page demonstrates the use of plot_metric_history which allows for the plotting the evolution of metrics over each population within a History object.

In [1]:

import paretobench as pb
from paretobench.plotting import plot_metric_history, population_obj_scatter
from paretobench.metrics import Hypervolume

import paretobench as pb from paretobench.plotting import plot_metric_history, population_obj_scatter from paretobench.metrics import Hypervolume

In [2]:

# Load an experiment and get the history object from it
exp = pb.Experiment.load("../data/NSGAII.h5")
hist = exp.runs[0]

# Load an experiment and get the history object from it exp = pb.Experiment.load("../data/NSGAII.h5") hist = exp.runs[0]

In [3]:

# Show the last generation
population_obj_scatter(hist.reports[-1])

# Show the last generation population_obj_scatter(hist.reports[-1])

Out[3]:

(<Figure size 640x480 with 1 Axes>, <Axes: xlabel='$f_1$', ylabel='$f_2$'>)

In [4]:

# Plot hypervolume vs function evaluations
plot_metric_history(exp.runs[0], Hypervolume(ref_point=[3, 3]))

# Plot hypervolume vs function evaluations plot_metric_history(exp.runs[0], Hypervolume(ref_point=[3, 3]))

Out[4]:

(<Figure size 640x480 with 1 Axes>,
 <Axes: xlabel='Function Evaluations', ylabel='Hypervolume'>)

In [5]:

# Plot hypervolume vs generation
plot_metric_history(exp.runs[0], Hypervolume(ref_point=[3, 3]), x_axis="generation")

# Plot hypervolume vs generation plot_metric_history(exp.runs[0], Hypervolume(ref_point=[3, 3]), x_axis="generation")

Out[5]:

(<Figure size 640x480 with 1 Axes>,
 <Axes: xlabel='Generation', ylabel='Hypervolume'>)