src/vis_utils.py

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 *
from saving_utils import download_from_hub



global data_component, filter_component



def benchmark_plot(benchmark_type, methods_selected, x_metric, y_metric):
    if benchmark_type == 'similarity':
        return plot_similarity_results(methods_selected, x_metric, y_metric)
    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")

    return 0

def get_method_color(method):
    return color_dict.get(method, 'black')  # If method is not in color_dict, use black


def get_labels_and_title(x_metric, y_metric):
    # Define mapping for long forms
    long_form_mapping = {
        "MF": "Molecular Function",
        "BP": "Biological Process",
        "CC": "Cellular Component"
    }
    
    # Parse the metrics
    def parse_metric(metric):
        parts = metric.split("_")
        dataset = parts[0]  # sparse/200/500
        category = parts[1]  # MF/BP/CC
        measure = parts[2]  # pvalue/correlation
        return dataset, category, measure
    
    x_dataset, x_category, x_measure = parse_metric(x_metric)
    y_dataset, y_category, y_measure = parse_metric(y_metric)
    
    # Determine the title
    if x_category == y_category:
        title = long_form_mapping[x_category]
    else:
        title = f"{long_form_mapping[x_category]} vs {long_form_mapping[y_category]}"
    
    # Determine the axis labels
    x_label = f"{x_measure.capitalize()} on {x_dataset.capitalize()} Dataset"
    y_label = f"{y_measure.capitalize()} on {y_dataset.capitalize()} Dataset"
    
    return title, x_label, y_label


def plot_similarity_results(methods_selected, x_metric, y_metric, similarity_path="/tmp/similarity_results.csv"):
    if not os.path.exists(similarity_path):
        benchmark_types = ["similarity", "function", "family", "affinity"] #download all files for faster results later
        download_from_hub(benchmark_types)
        
    similarity_df = pd.read_csv(similarity_path)
    
    # Filter the dataframe based on selected methods
    filtered_df = similarity_df[similarity_df['method_name'].isin(methods_selected)]

    # Add a new column to the dataframe for the color
    filtered_df['color'] = filtered_df['method_name'].apply(get_method_color)

    title, x_label, y_label = generate_labels_and_title(x_metric, y_metric)

    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=x_label, y=y_label)  # 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 = "/tmp"
    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)