From 82fa0f8e25cb2cc05cc047c2914f0b993257848e Mon Sep 17 00:00:00 2001
From: Ahmed Gad <ahmed.f.gad@gmail.com>
Date: Tue, 7 Jan 2025 12:37:53 -0500
Subject: [PATCH] Create the plot_pareto_front_curve() method to plot the
 pareto front curve

---
 pygad/visualize/plot.py | 101 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 101 insertions(+)

diff --git a/pygad/visualize/plot.py b/pygad/visualize/plot.py
index 7dffc4b..3ddeaff 100644
--- a/pygad/visualize/plot.py
+++ b/pygad/visualize/plot.py
@@ -384,3 +384,104 @@ def plot_genes(self,
         matplotlib.pyplot.show()
 
         return fig
+
+    def plot_pareto_front_curve(self, 
+                                title="Pareto Front Curve", 
+                                xlabel="Objective 1", 
+                                ylabel="Objective 2", 
+                                linewidth=3, 
+                                font_size=14, 
+                                label="Pareto Front",
+                                color="#FF6347",
+                                color_fitness="#4169E1",
+                                grid=True,
+                                alpha=0.7,
+                                marker="o",
+                                save_dir=None):
+        """
+        Creates, shows, and returns the pareto front curve. Can only be used with multi-objective problems.
+        It only works with 2 objectives. 
+        It also works only after completing at least 1 generation. If no generation is completed, an exception is raised.
+
+        Accepts the following:
+            title: Figure title.
+            xlabel: Label on the X-axis.
+            ylabel: Label on the Y-axis.
+            linewidth: Line width of the plot. Defaults to 3.
+            font_size: Font size for the labels and title. Defaults to 14.
+            label: The label used for the legend.
+            color: Color of the plot.
+            color_fitness: Color of the fitness points.
+            grid: Either True or False to control the visibility of the grid.
+            alpha: The transparency of the pareto front curve.
+            marker: The marker of the fitness points.
+            save_dir: Directory to save the figure.
+
+        Returns the figure.
+        """
+
+        if self.generations_completed < 1:
+            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.")
+            raise RuntimeError("The plot_pareto_front_curve() method can only be called after completing at least 1 generation but ({self.generations_completed}) is completed.")
+
+        if type(self.best_solutions_fitness[0]) in [list, tuple, numpy.ndarray] and len(self.best_solutions_fitness[0]) > 1:
+            # Multi-objective optimization problem.
+            if len(self.best_solutions_fitness[0]) == 2:
+                # Only 2 objectives. Proceed.
+                pass
+            else:
+                # More than 2 objectives.
+                self.logger.error(f"The plot_pareto_front_curve() method only supports 2 objectives but there are {self.best_solutions_fitness[0]} objectives.")
+                raise RuntimeError(f"The plot_pareto_front_curve() method only supports 2 objectives but there are {self.best_solutions_fitness[0]} objectives.")
+        else:
+            # Single-objective optimization problem.
+            self.logger.error("The plot_pareto_front_curve() method only works with multi-objective optimization problems.")
+            raise RuntimeError("The plot_pareto_front_curve() method only works with multi-objective optimization problems.")
+
+        # Plot the pareto front curve.
+        remaining_set = list(zip(range(0, self.last_generation_fitness.shape[0]), self.last_generation_fitness))
+        dominated_set, non_dominated_set = self.get_non_dominated_set(remaining_set)
+
+        # Extract the fitness values (objective values) of the non-dominated solutions for plotting.
+        pareto_front_x = [self.last_generation_fitness[item[0]][0] for item in dominated_set]
+        pareto_front_y = [self.last_generation_fitness[item[0]][1] for item in dominated_set]
+
+        # Sort the Pareto front solutions (optional but can make the plot cleaner)
+        sorted_pareto_front = sorted(zip(pareto_front_x, pareto_front_y))
+
+        # Plotting
+        fig = matplotlib.pyplot.figure()
+        # First, plot the scatter of all points (population)
+        all_points_x = [self.last_generation_fitness[i][0] for i in range(self.sol_per_pop)]
+        all_points_y = [self.last_generation_fitness[i][1] for i in range(self.sol_per_pop)]
+        matplotlib.pyplot.scatter(all_points_x, 
+                                  all_points_y, 
+                                  marker=marker,
+                                  color=color_fitness, 
+                                  label='Fitness', 
+                                  alpha=1.0)
+
+        # Then, plot the Pareto front as a curve
+        pareto_front_x_sorted, pareto_front_y_sorted = zip(*sorted_pareto_front)
+        matplotlib.pyplot.plot(pareto_front_x_sorted, 
+                               pareto_front_y_sorted, 
+                               marker=marker, 
+                               label=label, 
+                               alpha=alpha,
+                               color=color, 
+                               linewidth=linewidth)
+
+        matplotlib.pyplot.title(title, fontsize=font_size)
+        matplotlib.pyplot.xlabel(xlabel, fontsize=font_size)
+        matplotlib.pyplot.ylabel(ylabel, fontsize=font_size)
+        matplotlib.pyplot.legend()
+
+        matplotlib.pyplot.grid(grid)
+
+        if not save_dir is None:
+            matplotlib.pyplot.savefig(fname=save_dir, 
+                                      bbox_inches='tight')
+
+        matplotlib.pyplot.show()
+
+        return fig