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import pandas as pd
import os

import sys

script_dir = os.path.dirname(os.path.abspath(__file__))
sys.path.append('..')
sys.path.append('.')

def save_similarity_output(output_dict, method_name, leaderboard_path="./data/leaderboard_results.csv", similarity_path="./data/similarity_results.csv"):
    # Load or initialize the DataFrames
    if os.path.exists(leaderboard_path):
        leaderboard_df = pd.read_csv(leaderboard_path)
    else:
        leaderboard_df = pd.DataFrame()

    if os.path.exists(similarity_path):
        similarity_df = pd.read_csv(similarity_path)
    else:
        similarity_df = pd.DataFrame(columns=['Method'])

    # Check if method exists in similarity results
    if method_name not in similarity_df['Method'].values:
        similarity_df = pd.concat([similarity_df, pd.DataFrame({'Method': [method_name]})], ignore_index=True)
    
    # Initialize storage for averages
    averages = {}

    # Iterate through the output_dict and calculate averages if all aspects (MF, CC, BP) are present
    for dataset in ['sparse', '200', '500']:
        correlation_values = []
        pvalue_values = []

        # Check each aspect within the dataset (MF, BP, CC)
        for aspect in ['MF', 'BP', 'CC']:
            correlation_key = f"{dataset}_{aspect}_correlation"
            pvalue_key = f"{dataset}_{aspect}_pvalue"
            
            # Process correlation if present
            if correlation_key in output_dict:
                correlation_values.append(output_dict[correlation_key])
                similarity_df.at[similarity_df['Method'] == method_name, f"{dataset}_{aspect}_correlation"] = output_dict[correlation_key]
                leaderboard_df.at[0, f"sim_{dataset}_{aspect}_correlation"] = output_dict[correlation_key]
            
            # Process pvalue if present
            if pvalue_key in output_dict:
                pvalue_values.append(output_dict[pvalue_key])
                similarity_df.at[similarity_df['Method'] == method_name, f"{dataset}_{aspect}_pvalue"] = output_dict[pvalue_key]
                leaderboard_df.at[0, f"sim_{dataset}_{aspect}_pvalue"] = output_dict[pvalue_key]

        # Calculate averages if all three aspects (MF, BP, CC) are present
        if len(correlation_values) == 3:
            averages[f"{dataset}_Ave_correlation"] = sum(correlation_values) / 3
            similarity_df.at[similarity_df['Method'] == method_name, f"{dataset}_Ave_correlation"] = averages[f"{dataset}_Ave_correlation"]
            leaderboard_df.at[0, f"sim_{dataset}_Ave_correlation"] = averages[f"{dataset}_Ave_correlation"]

        if len(pvalue_values) == 3:
            averages[f"{dataset}_Ave_pvalue"] = sum(pvalue_values) / 3
            similarity_df.at[similarity_df['Method'] == method_name, f"{dataset}_Ave_pvalue"] = averages[f"{dataset}_Ave_pvalue"]
            leaderboard_df.at[0, f"sim_{dataset}_Ave_pvalue"] = averages[f"{dataset}_Ave_pvalue"]

    # Save the updated DataFrames back to CSV
    leaderboard_df.to_csv(leaderboard_path, index=False)
    similarity_df.to_csv(similarity_path, index=False)

    return 0

def save_function_output(model_output, method_name, func_results_path="./data/function_results.csv", leaderboard_path="./data/leaderboard_results.csv"):
    # Load or initialize the DataFrames
    if os.path.exists(func_results_path):
        func_results_df = pd.read_csv(func_results_path)
    else:
        func_results_df = pd.DataFrame(columns=['Method'])

    if os.path.exists(leaderboard_path):
        leaderboard_df = pd.read_csv(leaderboard_path)
    else:
        leaderboard_df = pd.DataFrame()

    # Ensure the method_name row exists in function results
    if method_name not in func_results_df['Method'].values:
        func_results_df = pd.concat([func_results_df, pd.DataFrame({'Method': [method_name]})], ignore_index=True)
    
    # Storage for averaging in leaderboard results
    metrics_sum = {
        'accuracy': {'BP': [], 'CC': [], 'MF': []},
        'F1': {'BP': [], 'CC': [], 'MF': []},
        'precision': {'BP': [], 'CC': [], 'MF': []},
        'recall': {'BP': [], 'CC': [], 'MF': []}
    }

    # Iterate over each entry in model_output
    for entry in model_output:
        key = entry[0]
        accuracy, f1, precision, recall = entry[1], entry[4], entry[7], entry[10]

        # Parse the key to extract the aspect and datasets
        aspect, dataset1, dataset2 = key.split('_')

        # Save each metric to function_results under its respective column
        func_results_df.at[func_results_df['Method'] == method_name, f"{aspect}_{dataset1}_{dataset2}_accuracy"] = accuracy
        func_results_df.at[func_results_df['Method'] == method_name, f"{aspect}_{dataset1}_{dataset2}_F1"] = f1
        func_results_df.at[func_results_df['Method'] == method_name, f"{aspect}_{dataset1}_{dataset2}_precision"] = precision
        func_results_df.at[func_results_df['Method'] == method_name, f"{aspect}_{dataset1}_{dataset2}_recall"] = recall

        # Add values for leaderboard averaging
        metrics_sum['accuracy'][aspect].append(accuracy)
        metrics_sum['F1'][aspect].append(f1)
        metrics_sum['precision'][aspect].append(precision)
        metrics_sum['recall'][aspect].append(recall)

    # Calculate averages for each aspect and overall (if all aspects have entries)
    for metric in ['accuracy', 'F1', 'precision', 'recall']:
        for aspect in ['BP', 'CC', 'MF']:
            if metrics_sum[metric][aspect]:
                aspect_average = sum(metrics_sum[metric][aspect]) / len(metrics_sum[metric][aspect])
                leaderboard_df.at[0, f"func_{aspect}_{metric}"] = aspect_average

        # Calculate overall average if each aspect has entries
        if all(metrics_sum[metric][aspect] for aspect in ['BP', 'CC', 'MF']):
            overall_average = sum(
                sum(metrics_sum[metric][aspect]) / len(metrics_sum[metric][aspect])
                for aspect in ['BP', 'CC', 'MF']
            ) / 3
            leaderboard_df.at[0, f"func_Ave_{metric}"] = overall_average

    # Save updated DataFrames to CSV
    func_results_df.to_csv(func_results_path, index=False)
    leaderboard_df.to_csv(leaderboard_path, index=False)

    return 0

def save_family_output(model_output, method_name, leaderboard_path="./data/leaderboard_results.csv", family_results_path="./data/family_results.csv"):
    # Load or initialize the DataFrames
    if os.path.exists(leaderboard_path):
        leaderboard_df = pd.read_csv(leaderboard_path)
    else:
        leaderboard_df = pd.DataFrame(columns=['Method'])

    if os.path.exists(family_results_path):
        family_results_df = pd.read_csv(family_results_path)
    else:
        family_results_df = pd.DataFrame(columns=['Method'])

    # Ensure the method_name row exists in the leaderboard results
    if method_name not in leaderboard_df['Method'].values:
        leaderboard_df = pd.concat([leaderboard_df, pd.DataFrame({'Method': [method_name]})], ignore_index=True)
    
    # Ensure the method_name row exists in family results
    if method_name not in family_results_df['Method'].values:
        family_results_df = pd.concat([family_results_df, pd.DataFrame({'Method': [method_name]})], ignore_index=True)

    # Iterate through the datasets and metrics
    for dataset, metrics in model_output.items():
        for metric, values in metrics.items():
            # Calculate the average for each metric in leaderboard results
            avg_value = sum(values) / len(values) if values else None
            leaderboard_df.at[leaderboard_df['Method'] == method_name, f"fam_{dataset}_{metric}_ave"] = avg_value

            # Save each fold result for family results
            for i, value in enumerate(values):
                family_results_df.at[family_results_df['Method'] == method_name, f"{dataset}_{metric}_{i}"] = value

    # Save updated DataFrames to CSV
    leaderboard_df.to_csv(leaderboard_path, index=False)
    family_results_df.to_csv(family_results_path, index=False)

    return leaderboard_df, family_results_df

def save_affinity_output(model_output, method_name, leaderboard_path="./data/leaderboard_results.csv", affinity_results_path="./data/affinity_results.csv"):
    # Load or initialize DataFrames
    if os.path.exists(leaderboard_path):
        leaderboard_df = pd.read_csv(leaderboard_path)
    else:
        leaderboard_df = pd.DataFrame(columns=['Method'])

    if os.path.exists(affinity_results_path):
        affinity_results_df = pd.read_csv(affinity_results_path)
    else:
        affinity_results_df = pd.DataFrame(columns=['Method'])

    # Ensure the method_name row exists in the leaderboard results
    if method_name not in leaderboard_df['Method'].values:
        leaderboard_df = pd.concat([leaderboard_df, pd.DataFrame({'Method': [method_name]})], ignore_index=True)
    
    # Ensure the method_name row exists in affinity results
    if method_name not in affinity_results_df['Method'].values:
        affinity_results_df = pd.concat([affinity_results_df, pd.DataFrame({'Method': [method_name]})], ignore_index=True)

    # Process 'summary' section for leaderboard results
    summary = model_output.get('summary', {})
    if summary:
        leaderboard_df.at[leaderboard_df['Method'] == method_name, 'aff_mse_ave'] = summary.get('val_mse_error')
        leaderboard_df.at[leaderboard_df['Method'] == method_name, 'aff_mae_ave'] = summary.get('val_mae_error')
        leaderboard_df.at[leaderboard_df['Method'] == method_name, 'aff_corr_ave'] = summary.get('validation_corr')

    # Process 'detail' section for affinity results
    detail = model_output.get('detail', {})
    if detail:
        # Save each 10-fold cross-validation result for mse, mae, and corr
        for i in range(10):
            if 'val_mse_errors' in detail:
                affinity_results_df.at[affinity_results_df['Method'] == method_name, f"mse_{i}"] = detail['val_mse_errors'][i]
            if 'val_mae_errors' in detail:
                affinity_results_df.at[affinity_results_df['Method'] == method_name, f"mae_{i}"] = detail['val_mae_errors'][i]
            if 'validation_corrs' in detail:
                affinity_results_df.at[affinity_results_df['Method'] == method_name, f"corr_{i}"] = detail['validation_corrs'][i]

    # Save updated DataFrames to CSV
    leaderboard_df.to_csv(leaderboard_path, index=False)
    affinity_results_df.to_csv(affinity_results_path, index=False)

    return 0