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 import sys script_dir = os.path.dirname(os.path.abspath(__file__)) sys.path.append('..') sys.path.append('.') from about import * global data_component, filter_component 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 get_method_color(method): return color_dict.get(method, 'black') # If method is not in color_dict, use black def set_colors_and_marks_for_representation_groups(ax): for label in ax.get_xticklabels(): text = label.get_text() color = group_color_dict.get(text, 'black') # Default to black if label not in dict label.set_color(color) label.set_fontweight('bold') # Add a caret symbol to specific labels if text in {'MUT2VEC', 'PFAM', 'GENE2VEC', 'BERT-PFAM'}: label.set_text(f"^ {text}") def benchmark_plot(benchmark_type, methods_selected, x_metric, y_metric): if benchmark_type == 'similarity': title = f"{x_metric} vs {y_metric}" return plot_similarity_results(methods_selected, x_metric, y_metric, title) elif benchmark_type == 'function': return plot_function_results("./data/function_results.csv", x_metric, y_metric, methods_selected) elif benchmark_type == 'family': return plot_family_results("./data/family_results.csv", methods_selected, x_metric, save_path="./plot_images") elif benchmark_type == "affinity": return plot_affinity_results("./data/affinity_results.csv", methods_selected, x_metric, save_path="./plot_images") else: # Use general visualizer logic return general_visualizer_plot(methods_selected, x_metric=x_metric, y_metric=y_metric) 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 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)] 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_name')) # 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 plot_function_results(file_path, aspect, metric, method_names): # Load data df = pd.read_csv(file_path) # Filter for selected methods df = df[df['Method'].isin(method_names)] # Filter columns for specified aspect and metric columns_to_plot = [col for col in df.columns if col.startswith(f"{aspect}_") and col.endswith(f"_{metric}")] df = df[['Method'] + columns_to_plot] df.set_index('Method', inplace=True) # Create clustermap g = sns.clustermap(df, annot=True, cmap="YlGnBu", row_cluster=False, col_cluster=False, figsize=(15, 15)) # Get heatmap axis and customize labels ax = g.ax_heatmap ax.set_xlabel("") ax.set_ylabel("") # Apply color and caret adjustments to x-axis labels 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 def plot_affinity_results(file_path, method_names, metric, save_path="./plot_images"): # Load the CSV data df = pd.read_csv(file_path) # Filter for selected methods df = df[df['Method'].isin(method_names)] # Gather columns related to the specified metric and validate metric_columns = [col for col in df.columns if col.startswith(f"{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 for sorting # Set up the plot sns.set(rc={'figure.figsize': (13.7, 8.27)}) sns.set_theme(style="whitegrid", color_codes=True) # Create a boxplot for the metric 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(5)) ax.get_xaxis().set_minor_locator(mpl.ticker.AutoMinorLocator()) ax.get_yaxis().set_minor_locator(mpl.ticker.AutoMinorLocator()) ax.grid(b=True, which='major', color='gainsboro', linewidth=1.0) ax.grid(b=True, which='minor', color='whitesmoke', linewidth=0.5) # Apply custom color settings to y-axis labels set_colors_and_marks_for_representation_groups(ax) # Ensure save path exists os.makedirs(save_path, exist_ok=True) # Save the plot filename = os.path.join(save_path, f"{metric}_affinity_results.png") ax.get_figure().savefig(filename, dpi=400, bbox_inches='tight') plt.close() # Close the plot to free memory return filename def update_metric_choices(benchmark_type): if benchmark_type == 'similarity': # Show x and y metric selectors for similarity metric_names = benchmark_specific_metrics.get(benchmark_type, []) return ( gr.update(choices=metric_names, value=metric_names[0], visible=True), gr.update(choices=metric_names, value=metric_names[1], visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False) ) elif benchmark_type == 'function': # Show aspect and dataset type selectors for function aspect_types = benchmark_specific_metrics[benchmark_type]['aspect_types'] dataset_types = benchmark_specific_metrics[benchmark_type]['dataset_types'] return ( gr.update(visible=False), gr.update(visible=False), gr.update(choices=aspect_types, value=aspect_types[0], visible=True), gr.update(choices=dataset_types, value=dataset_types[0], visible=True), gr.update(visible=False), gr.update(visible=False) ) elif benchmark_type == 'family': # Show dataset and metric selectors for family datasets = benchmark_specific_metrics[benchmark_type]['datasets'] metrics = benchmark_specific_metrics[benchmark_type]['metrics'] return ( gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(choices=datasets, value=datasets[0], visible=True), gr.update(choices=metrics, value=metrics[0], visible=True) ) elif benchmark_type == 'affinity': # Show single metric selector for affinity metrics = benchmark_specific_metrics[benchmark_type] return ( gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(choices=metrics, value=metrics[0], visible=True) ) return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)