scripts/build-table.py

import os
import json
import pandas as pd
from tabulate import tabulate
import typer

def generate_detailed_markdown_chart():
    # Directories for each model type in the desired order
    model_dirs = [
        ('small', 'training/sm'),
        ('medium', 'training/md'),
        ('large', 'training/lg'),
        ('transformer', 'training/trf')
    ]

    # DataFrame to hold the overall data
    overall_df = pd.DataFrame(columns=['Model', 'Precision', 'Recall', 'F-Score'])
    
    # DataFrame to hold the per-type data
    per_type_df = pd.DataFrame(columns=['Model', 'Label', 'Precision', 'Recall', 'F-Score'])

    for model_name, dir_path in model_dirs:
        metrics_file = os.path.join(dir_path, 'metrics.json')

        # Check if the file exists
        if os.path.exists(metrics_file):
            with open(metrics_file, 'r') as file:
                metrics = json.load(file)
                # Extract overall metrics
                overall_df = overall_df.append({
                    'Model': model_name.capitalize(),
                    'Precision': round(metrics['spans_sc_p'] * 100, 1),
                    'Recall': round(metrics['spans_sc_r'] * 100, 1),
                    'F-Score': round(metrics['spans_sc_f'] * 100, 1)
                }, ignore_index=True)

                # Extract per-type metrics
                for label, scores in metrics.get('spans_sc_per_type', {}).items():
                    per_type_df = per_type_df.append({
                        'Model': model_name.capitalize(),
                        'Label': label,
                        'Precision': round(scores['p'] * 100, 1),
                        'Recall': round(scores['r'] * 100, 1),
                        'F-Score': round(scores['f'] * 100, 1)
                    }, ignore_index=True)

    # Define the order for models
    model_order = ['Small', 'Medium', 'Large', 'Transformer']
    per_type_df['Model'] = pd.Categorical(per_type_df['Model'], categories=model_order, ordered=True)

    # Sort the per_type_df first by Label, then by Model
    per_type_df.sort_values(by=['Label', 'Model'], inplace=True)

    # Convert the DataFrames to Markdown
    overall_markdown = tabulate(overall_df, headers='keys', tablefmt='pipe', showindex=False)
    per_type_markdown = tabulate(per_type_df, headers='keys', tablefmt='pipe', showindex=False)

    # Write the Markdown tables to a file
    with open('model_comparison.md', 'w') as md_file:
        md_file.write("# Overall Model Performance\n")
        md_file.write(overall_markdown)
        md_file.write("\n\n# Performance per Label\n")
        md_file.write(per_type_markdown)

    print("Markdown chart created as 'model_comparison.md'")

if __name__ == "__main__":
    typer.run(generate_detailed_markdown_chart)