Update src/vis_utils.py

src/vis_utils.py

@@ -44,10 +44,10 @@ def benchmark_plot(benchmark_type, methods_selected, x_metric, y_metric):
     elif benchmark_type == 'similarity':
         title = f"{x_metric} vs {y_metric}"
         return draw_scatter_plot_similarity(methods_selected, x_metric, y_metric, title)
-    elif benchmark_type == 'Benchmark 3':
-        return benchmark_3_plot(x_metric, y_metric)
-    elif benchmark_type == 'Benchmark 4':
-        return benchmark_4_plot(x_metric, y_metric)
+    elif benchmark_type == 'function':
+        plot_function_results("./data/function_results.csv", x_metric, y_metric, methods_selected)
+    elif benchmark_type == 'family':
+        plot_family_results("./data/family_results.csv", methods_selected, x_metric, save_path="./plot_images")
     else:
         return "Invalid benchmark type selected."
 
@@ -78,7 +78,7 @@ def general_visualizer(methods_selected, x_metric, y_metric):
     
     return plot_path
 
-def draw_scatter_plot_similarity(methods_selected, x_metric, y_metric, title):
+def plot_similarity_results(methods_selected, x_metric, y_metric, title):
     df = pd.read_csv(CSV_RESULT_PATH)
     # Filter the dataframe based on selected methods
     filtered_df = df[df['method_name'].isin(methods_selected)]
@@ -122,7 +122,7 @@ def draw_scatter_plot_similarity(methods_selected, x_metric, y_metric, title):
     
     return filename
 
-def visualize_aspect_metric_clustermap(file_path, aspect, metric, method_names):
+def plot_function_results(file_path, aspect, metric, method_names):
     # Load data
     df = pd.read_csv(file_path)
     
@@ -146,9 +146,58 @@ def visualize_aspect_metric_clustermap(file_path, aspect, metric, method_names):
     set_colors_and_marks_for_representation_groups(ax)
 
     # Save the plot as an image
+    save_path = "./plot_images"  # Ensure this folder exists or adjust the path
     os.makedirs(save_path, exist_ok=True)  # Create directory if it doesn't exist
     filename = os.path.join(save_path, f"{aspect}_{metric}_heatmap.png")
     plt.savefig(filename, dpi=400, bbox_inches='tight')
     plt.close()  # Close the plot to free memory
     
+    return filename
+
+def plot_family_results(file_path, method_names, metric, save_path="./plot_images"):
+    # Load data
+    df = pd.read_csv(file_path)
+    
+    # Filter by method names and selected metric columns
+    df = df[df['Method'].isin(method_names)]
+    metric_columns = [col for col in df.columns if col.startswith(f"{metric}_")]
+    
+    # Check if there are columns matching the selected metric
+    if not metric_columns:
+        print(f"No columns found for metric '{metric}'.")
+        return None
+    
+    # Reshape data for plotting
+    df_long = pd.melt(df[['Method'] + metric_columns], id_vars=['Method'], var_name='Fold', value_name='Value')
+    df_long['Fold'] = df_long['Fold'].apply(lambda x: int(x.split('_')[-1]))  # Extract fold index
+
+    # Set up the plot
+    sns.set(rc={'figure.figsize': (13.7, 18.27)})
+    sns.set_theme(style="whitegrid", color_codes=True)
+    ax = sns.boxplot(data=df_long, x='Value', y='Method', hue='Fold', whis=np.inf, orient="h")
+    
+    # Customize x-axis and y-axis tickers and grid
+    ax.xaxis.set_major_locator(ticker.MultipleLocator(0.2))
+    ax.get_xaxis().set_minor_locator(ticker.AutoMinorLocator())
+    ax.get_yaxis().set_minor_locator(ticker.AutoMinorLocator())
+    ax.grid(b=True, which='major', color='gainsboro', linewidth=1.0)
+    ax.grid(b=True, which='minor', color='whitesmoke', linewidth=0.5)
+    ax.set_xlim(0, 1)
+
+    # Draw dashed lines between different representations on y-axis
+    yticks = ax.get_yticks()
+    for ytick in yticks:
+        ax.hlines(ytick + 0.5, -0.1, 1, linestyles='dashed')
+
+    # Apply color settings to y-axis labels
+    set_colors_and_marks_for_representation_groups(ax)
+    
+    # Ensure save directory exists
+    os.makedirs(save_path, exist_ok=True)
+    
+    # Save the plot
+    filename = os.path.join(save_path, f"{metric}_family_results.png")
+    ax.get_figure().savefig(filename, dpi=400, bbox_inches='tight')
+    plt.close()  # Close the plot to free memory
+
     return filename