Update src/vis_utils.py

src/vis_utils.py

@@ -0,0 +1,107 @@
+import gradio as gr
+import pandas as pd
+import re
+import os
+import json
+import yaml
+import matplotlib.pyplot as plt
+import seaborn as sns
+import plotnine as p9
+
+from about import *
+global data_component, filter_component
+
+def get_method_color(method):
+    return color_dict.get(method, 'black')  # If method is not in color_dict, use black
+
+def draw_scatter_plot_similarity(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)]
+
+    def get_method_color(method):
+        return color_dict.get(method.upper(), 'black')
+
+    # Add a new column to the dataframe for the color
+    filtered_df['color'] = filtered_df['method_name'].apply(get_method_color)
+
+    adjust_text_dict = {
+        'expand_text': (1.15, 1.4), 'expand_points': (1.15, 1.25), 'expand_objects': (1.05, 1.5),
+        'expand_align': (1.05, 1.2), 'autoalign': 'xy', 'va': 'center', 'ha': 'center',
+        'force_text': (.0, 1.), 'force_objects': (.0, 1.),
+        'lim': 500000, 'precision': 1., 'avoid_points': True, 'avoid_text': True
+    }
+
+    # Create the scatter plot using plotnine (ggplot)
+    g = (p9.ggplot(data=filtered_df,
+                   mapping=p9.aes(x=x_metric,  # Use the selected x_metric
+                                  y=y_metric,  # Use the selected y_metric
+                                  color='color',  # Use the dynamically generated color
+                                  label='method_names'))  # Label each point by the method name
+         + p9.geom_point(size=3)  # Add points with no jitter, set point size
+         + p9.geom_text(nudge_y=0.02, size=8)  # Add method names as labels, nudge slightly above the points
+         + p9.labs(title=title, x=f"{x_metric}", y=f"{y_metric}")  # Dynamic labels for X and Y axes
+         + p9.scale_color_identity()  # Use colors directly from the dataframe
+         + p9.theme(legend_position='none', 
+                    figure_size=(8, 8),  # Set figure size
+                    axis_text=p9.element_text(size=10),   
+                    axis_title_x=p9.element_text(size=12),
+                    axis_title_y=p9.element_text(size=12))
+    )
+
+    # 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, title.replace(" ", "_") + "_Similarity_Scatter.png")
+    
+    g.save(filename=filename, dpi=400)
+    
+    return filename
+
+def benchmark_plot(benchmark_type, methods_selected, x_metric, y_metric):
+    if benchmark_type == 'flexible':
+        # Use general visualizer logic
+        return general_visualizer_plot(methods_selected, x_metric=x_metric, y_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)
+    else:
+        return "Invalid benchmark type selected."
+
+
+def get_baseline_df(selected_methods, selected_metrics):
+    df = pd.read_csv(CSV_RESULT_PATH)
+    present_columns = ["method_name"] + selected_metrics
+    df = df[df['method_name'].isin(selected_methods)][present_columns]
+    return df
+
+def general_visualizer(methods_selected, x_metric, y_metric):
+    df = pd.read_csv(CSV_RESULT_PATH)
+    filtered_df = df[df['method_name'].isin(methods_selected)]
+
+    # Create a Seaborn lineplot with method as hue
+    plt.figure(figsize=(10, 8))  # Increase figure size
+    sns.lineplot(
+        data=filtered_df, 
+        x=x_metric, 
+        y=y_metric, 
+        hue="method_name",  # Different colors for different methods
+        marker="o",  # Add markers to the line plot
+    )
+    
+    # Add labels and title
+    plt.xlabel(x_metric)
+    plt.ylabel(y_metric)
+    plt.title(f'{y_metric} vs {x_metric} for selected methods')
+    plt.grid(True)
+    
+    # Save the plot to display it in Gradio
+    plot_path = "plot.png"
+    plt.savefig(plot_path)
+    plt.close()
+    
+    return plot_path