Small fixes

examples/demo1.py

@@ -38,7 +38,7 @@ def main():
     os.chdir(script_dir)
 
     # Filepath
-    file_path = os.path.join("datasets", "corners_3clusters.csv")
+    file_path = os.path.join("datasets", "corners_5clusters.csv")
 
     # Generate data with corners distribution and save it to a CSV file
     data_with_labels = DataGenerator.generate_data_corners(

src/gaufs/data_generator.py

@@ -588,6 +588,9 @@ def _generate_cluster_centered(
             # significant dimensions
             # Generate random direction vector
             denom = 0.0
+            # avoid division by zero in case the random vector is zero
+            denom = 1.0 if denom == 0.0 else denom
+
             for j in range(num_dims_sig):
                 vector_aux[j] = random.uniform(-1, 1)
                 denom = denom + vector_aux[j] ** 2

src/gaufs/gaufs.py

@@ -834,7 +834,7 @@ def run(self):
                     f"Optimal number of clusters: {self._optimal_variable_selection_and_num_of_clusters[1]}\n"
                 )
                 f.write(
-                    f"Fitness: {self._optimal_variable_selection_and_num_of_clusters}\n\n"
+                    f"Fitness: {self._fitness_of_optimal_variable_selection_and_num_of_clusters}\n\n"
                 )
                 f.write("Selected Variables:\n")
                 f.write(
@@ -888,7 +888,7 @@ def run(self):
 
         return (
             self._optimal_variable_selection_and_num_of_clusters,
-            self._optimal_variable_selection_and_num_of_clusters,
+            self._fitness_of_optimal_variable_selection_and_num_of_clusters,
         )
 
     def run_genetic_searches(self):
@@ -1002,7 +1002,7 @@ def analyze_variable_weights(self):
                 "Variable significances not computed. Please run genetic searches with `run_genetic_searches` before analyzing variable weights."
             )
 
-        thresholds = sorted(self._variable_significance, reverse=True)
+        thresholds = sorted(set(self._variable_significance), reverse=True)
 
         # list of the considered selections
         possible_variable_selections = []
@@ -1228,7 +1228,9 @@ def plot_dictionaries(self):
         _plot_discontinuous(ax1, x1, y1, marker="o", color="black")
         ax1.set_xlabel("Number of Selected Variables")
         ax1.set_ylabel("Number of Clusters")
-        ax1.set_title("Number of Clusters for Each Selection")
+        ax1.set_title(
+            r"Number of Clusters for Each Selection $\Psi_{\text{num\_clusters}}$"
+        )
         ax1.grid(True, alpha=0.3)
         ax1.xaxis.set_major_locator(plt.MaxNLocator(integer=True))
         ax1.yaxis.set_major_locator(plt.MaxNLocator(integer=True))
@@ -1239,7 +1241,7 @@ def plot_dictionaries(self):
         ax2.plot(x2, y2, marker="o", color="tab:blue")
         ax2.set_xlabel("Number of Selected Variables")
         ax2.set_ylabel("Fitness")
-        ax2.set_title("Fitness for Each Selection")
+        ax2.set_title(r"Fitness for Each Selection $\tilde{\Psi}_{\text{fitness}}$")
         ax2.grid(True, alpha=0.3)
         ax2.xaxis.set_major_locator(plt.MaxNLocator(integer=True))
 
@@ -1249,15 +1251,19 @@ def plot_dictionaries(self):
         ax3.plot(x3, y3, marker="o", color="tab:blue")
         ax3.set_xlabel("Number of Selected Variables")
         ax3.set_ylabel("Threshold")
-        ax3.set_title("Threshold for Each Selection")
+        ax3.set_title(r"Threshold for Each Selection $\tilde{\Psi}_{\text{weight}}$")
         ax3.grid(True, alpha=0.3)
         ax3.xaxis.set_major_locator(plt.MaxNLocator(integer=True))
 
         # Plot 4: Importance (continuous line) and Delta Importance (red crosses)
         ax4 = axes[1, 1]
         x4, y4 = zip(*sorted(self._dict_num_var_selected_importance.items()))
         ax4.plot(
-            x4, y4, marker="o", label="Selected Variables' Importance", color="navy"
+            x4,
+            y4,
+            marker="o",
+            label=r"Selected Variables' Importance $\Phi_{\text{average}}$",
+            color="navy",
         )
 
         x5, y5 = zip(
@@ -1271,15 +1277,15 @@ def plot_dictionaries(self):
             marker="x",
             s=50,
             color="red",
-            label="Delta Importance with Exp Decay",
+            label=r"Delta Importance with Exp Decay $\tilde{\delta}_i$",
         )
 
         x_argmax = sum(self._optimal_variable_selection_and_num_of_clusters[0])
         ax4.axvline(
             x=x_argmax,
             color="black",
             linestyle="--",
-            label=f"Automatic solution with {x_argmax} variables achieving a fitness of: {self._dict_num_var_selected_fitness[x_argmax]:.3f}",
+            label=rf"Automatic solution: {x_argmax} = $\hat{{i}}$ variables and fitness of: {self._dict_num_var_selected_fitness[x_argmax]:.3f}",
         )
 
         ax4.set_xlabel("Number of Selected Variables")
@@ -1462,7 +1468,6 @@ def get_plot_comparing_solution_with_another_metric(
             label=f"Automatic solution with {x_argmax} variables achieving a fitness of: {self._dict_num_var_selected_fitness[x_argmax]:.3f}",
         )
         ax1.xaxis.set_major_locator(plt.MaxNLocator(integer=True))
-        ax1.legend()
 
         # Right plot: Variables vs External Metric
         # plot with discontinuities
@@ -1487,7 +1492,6 @@ def get_plot_comparing_solution_with_another_metric(
             label=f"Automatic solution with {x_argmax} variables achieving a metric of: {external_metrics[num_vars.index(x_argmax)]:.3f}",
         )
         ax2.xaxis.set_major_locator(plt.MaxNLocator(integer=True))
-        ax2.legend()
 
         # Add baseline scores for true number of labels
         if true_number_of_labels is not None:
@@ -1511,8 +1515,8 @@ def get_plot_comparing_solution_with_another_metric(
                 color="red",
                 label="Metric for each selection with True Number of Labels",
             )
-            ax1.legend()
-            ax2.legend()
+        ax1.legend()
+        ax2.legend()
 
         plt.tight_layout()
 

src/gaufs/genetic_search.py

@@ -91,7 +91,6 @@ def __init__(
         self.num_clusters_and_its_max_fitness = dict()
         # dictionary={variable_selection:(maximum fitness found for that selection (regardeless of the cluster number), number of times a chromosome with this selection entered in the hof)}
         self.hof_counter = dict()
-        self.hof_weighted = dict()
 
         # logbook to store statistics
         self.logbook = tools.Logbook()
@@ -336,7 +335,7 @@ def run(self):
                     self.hof_counter[variables_selection] = (
                         max(old[0], ind.fitness.values[0]),
                         old[1] + 1,
-                    )  # maximo fitness y cuento las veces que ha entrado
+                    )  # max fitness and times it entered in the hof
                 else:
                     self.hof_counter[variables_selection] = (ind.fitness.values[0], 1)
 

src/gaufs/utils.py

@@ -246,15 +246,22 @@ def _clear_directory(directory_path):
 # Helper function to plot with discontinuities
 def _plot_discontinuous(ax, x_vals, y_vals, **kwargs):
     """Plot lines only between consecutive x values"""
+    # Extract label from kwargs
+    label = kwargs.pop("label", None)
     i = 0
     while i < len(x_vals):
         # Find the end of the consecutive sequence
         j = i
         while j < len(x_vals) - 1 and x_vals[j + 1] == x_vals[j] + 1:
             j += 1
 
-        # Plot this consecutive segment
-        ax.plot(x_vals[i : j + 1], y_vals[i : j + 1], **kwargs)
+        # Plot this consecutive segment: only the first segment carries the label
+        ax.plot(
+            x_vals[i : j + 1],
+            y_vals[i : j + 1],
+            label=label if i == 0 else None,
+            **kwargs,
+        )
 
         # Move to next segment
         i = j + 1