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Commit dd07de9

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Merge pull request #310 from ahmedfgad/github-actions
Create the plot_pareto_front_curve() method to plot the pareto front ...
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‎pygad/visualize/plot.py

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@@ -384,3 +384,104 @@ def plot_genes(self,
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matplotlib.pyplot.show()
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return fig
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def plot_pareto_front_curve(self,
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title="Pareto Front Curve",
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xlabel="Objective 1",
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ylabel="Objective 2",
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linewidth=3,
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font_size=14,
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label="Pareto Front",
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color="#FF6347",
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color_fitness="#4169E1",
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grid=True,
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alpha=0.7,
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marker="o",
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save_dir=None):
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"""
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Creates, shows, and returns the pareto front curve. Can only be used with multi-objective problems.
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It only works with 2 objectives.
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It also works only after completing at least 1 generation. If no generation is completed, an exception is raised.
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Accepts the following:
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title: Figure title.
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xlabel: Label on the X-axis.
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ylabel: Label on the Y-axis.
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linewidth: Line width of the plot. Defaults to 3.
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font_size: Font size for the labels and title. Defaults to 14.
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label: The label used for the legend.
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color: Color of the plot.
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color_fitness: Color of the fitness points.
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grid: Either True or False to control the visibility of the grid.
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alpha: The transparency of the pareto front curve.
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marker: The marker of the fitness points.
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save_dir: Directory to save the figure.
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Returns the figure.
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"""
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if self.generations_completed < 1:
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self.logger.error("The plot_pareto_front_curve() method can only be called after completing at least 1 generation but ({self.generations_completed}) is completed.")
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raise RuntimeError("The plot_pareto_front_curve() method can only be called after completing at least 1 generation but ({self.generations_completed}) is completed.")
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if type(self.best_solutions_fitness[0]) in [list, tuple, numpy.ndarray] and len(self.best_solutions_fitness[0]) > 1:
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# Multi-objective optimization problem.
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if len(self.best_solutions_fitness[0]) == 2:
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# Only 2 objectives. Proceed.
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pass
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else:
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# More than 2 objectives.
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self.logger.error(f"The plot_pareto_front_curve() method only supports 2 objectives but there are {self.best_solutions_fitness[0]} objectives.")
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raise RuntimeError(f"The plot_pareto_front_curve() method only supports 2 objectives but there are {self.best_solutions_fitness[0]} objectives.")
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else:
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# Single-objective optimization problem.
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self.logger.error("The plot_pareto_front_curve() method only works with multi-objective optimization problems.")
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raise RuntimeError("The plot_pareto_front_curve() method only works with multi-objective optimization problems.")
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# Plot the pareto front curve.
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remaining_set = list(zip(range(0, self.last_generation_fitness.shape[0]), self.last_generation_fitness))
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dominated_set, non_dominated_set = self.get_non_dominated_set(remaining_set)
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# Extract the fitness values (objective values) of the non-dominated solutions for plotting.
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pareto_front_x = [self.last_generation_fitness[item[0]][0] for item in dominated_set]
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pareto_front_y = [self.last_generation_fitness[item[0]][1] for item in dominated_set]
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# Sort the Pareto front solutions (optional but can make the plot cleaner)
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sorted_pareto_front = sorted(zip(pareto_front_x, pareto_front_y))
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# Plotting
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fig = matplotlib.pyplot.figure()
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# First, plot the scatter of all points (population)
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all_points_x = [self.last_generation_fitness[i][0] for i in range(self.sol_per_pop)]
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all_points_y = [self.last_generation_fitness[i][1] for i in range(self.sol_per_pop)]
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matplotlib.pyplot.scatter(all_points_x,
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all_points_y,
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marker=marker,
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color=color_fitness,
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label='Fitness',
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alpha=1.0)
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# Then, plot the Pareto front as a curve
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pareto_front_x_sorted, pareto_front_y_sorted = zip(*sorted_pareto_front)
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matplotlib.pyplot.plot(pareto_front_x_sorted,
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pareto_front_y_sorted,
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marker=marker,
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label=label,
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alpha=alpha,
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color=color,
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linewidth=linewidth)
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matplotlib.pyplot.title(title, fontsize=font_size)
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matplotlib.pyplot.xlabel(xlabel, fontsize=font_size)
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matplotlib.pyplot.ylabel(ylabel, fontsize=font_size)
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matplotlib.pyplot.legend()
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matplotlib.pyplot.grid(grid)
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if not save_dir is None:
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matplotlib.pyplot.savefig(fname=save_dir,
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bbox_inches='tight')
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matplotlib.pyplot.show()
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return fig

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