| |
| import pandas as pd |
| import numpy as np |
| from pathlib import Path |
|
|
|
|
| def aggregate_llm_share(): |
| base_dir = Path(__file__).parent |
|
|
| series_order = [ |
| ( |
| "Email Responder", |
| ["EmailResponder", "EmailResponder-MCP"], |
| ), |
| ( |
| "Recruitment", |
| [ |
| "RecruitmentAssistant-A2A", |
| "RecruitmentAssistant-H_A2A", |
| "RecruitmentAssistant-MCP", |
| ], |
| ), |
| ("Markdown Val.", ["MarkdownValidator", "MarkdownValidator-MCP"]), |
| ("Game Builder", ["GameBuilder", "GameBuilder-MCP"]), |
| ( |
| "SQL Asst.", |
| ["SQLAssistant-A2A", "SQLAssistant-H_A2A", "SQLAssistant-MCP"], |
| ), |
| ( |
| "Landing Pg.", |
| [ |
| "LandingPageGenerator-A2A", |
| "LandingPageGenerator-H_A2A", |
| "LandingPageGenerator-MCP", |
| ], |
| ), |
| ( |
| "Book Writer", |
| [ |
| "BookWriter-A2A", |
| "BookWriter-H_A2A", |
| "BookWriter-MCP", |
| ], |
| ), |
| ( |
| "Social M. M.", |
| [ |
| "SocialMediaManager-A2A", |
| "SocialMediaManager-H_A2A", |
| "SocialMediaManager-MCP", |
| ], |
| ), |
| ] |
|
|
| projects = [] |
| csv_files = [] |
|
|
| project_to_display_name = {} |
|
|
| for series_name, project_list in series_order: |
| for project_name in project_list: |
| project_dir = base_dir / project_name |
| if project_dir.is_dir(): |
| csv_file = project_dir / "performance_breakdown_summary_by_model.csv" |
| if csv_file.exists(): |
| if "-A2A_mix" in project_name: |
| display_name = f"{series_name} (A2A_mix)" |
| elif "-A2A" in project_name: |
| display_name = f"{series_name} (A2A)" |
| elif "-MCP" in project_name: |
| display_name = f"{series_name} (MCP)" |
| else: |
| |
| display_name = f"{series_name} (CrewAI)" |
|
|
| projects.append(project_name) |
| project_to_display_name[project_name] = display_name |
| csv_files.append(csv_file) |
| else: |
| print(f"Warning: {project_name} missing CSV file") |
|
|
| print(f"Found {len(projects)} projects with CSV files") |
|
|
| models = [ |
| "GPT-5", |
| "GPT-4o-mini", |
| "DeepSeek-V3-1", |
| "DeepSeek-R1", |
| "Gemini-2.5-flash", |
| "Gemini-2.5-flash-nothinking", |
| "Qwen3-235b", |
| ] |
|
|
| data_dict = {model: [] for model in models} |
| weight_dict = {model: [] for model in models} |
| total_weighted_sum = 0.0 |
| total_weight = 0.0 |
|
|
| for project, csv_file in zip(projects, csv_files): |
| try: |
| df = pd.read_csv(csv_file) |
|
|
| for model in models: |
| model_data = df[df["model"] == model] |
| if not model_data.empty: |
| llm_share = model_data["LLM_share"].values[0] |
| llm_share = min(llm_share, 1.0) |
| llm_share = round(llm_share, 4) |
| data_dict[model].append(llm_share) |
|
|
| comp_time = model_data["total_components_time"].values[0] |
| weight = float(comp_time) if pd.notna(comp_time) else 0.0 |
| weight_dict[model].append(weight) |
|
|
| if pd.notna(llm_share) and weight > 0: |
| total_weighted_sum += llm_share * weight |
| total_weight += weight |
| else: |
| data_dict[model].append(None) |
| weight_dict[model].append(0.0) |
| except Exception as e: |
| print(f"Error reading {csv_file}: {e}") |
| for model in models: |
| data_dict[model].append(None) |
| weight_dict[model].append(0.0) |
|
|
| result_df = pd.DataFrame(data_dict, index=projects) |
| result_df.index = result_df.index.map(project_to_display_name) |
| result_df.index.name = "Model" |
| result_df = result_df.T |
|
|
| |
| overall_avg = ( |
| round(total_weighted_sum / total_weight, 4) if total_weight > 0 else np.nan |
| ) |
| overall_row_name = "Overall Average (time-weighted, all models & projects)" |
| overall_row = pd.Series( |
| {col: overall_avg for col in result_df.columns}, name=overall_row_name |
| ) |
| result_df = pd.concat([result_df, overall_row.to_frame().T]) |
|
|
| output_file = base_dir / "llm_share_summary.csv" |
| result_df.to_csv(output_file, float_format="%.4f") |
| print(f"\nCSV saved to: {output_file}") |
| print(f"\nShape: {result_df.shape[0]} models × {result_df.shape[1]} projects") |
|
|
| return result_df |
|
|
|
|
| def generate_latex_table(df): |
| num_cols = len(df.columns) |
|
|
| latex = [] |
| latex.append("\\begin{table*}[htbp]") |
| latex.append("\\centering") |
| latex.append("\\small") |
| latex.append(f"\\begin{{tabular}}{{l{'c' * num_cols}}}") |
| latex.append("\\toprule") |
|
|
| header = "Model & " + " & ".join(df.columns) + " \\\\" |
| latex.append(header) |
| latex.append("\\midrule") |
|
|
| for idx, row in df.iterrows(): |
| row_str = ( |
| str(idx) |
| + " & " |
| + " & ".join([f"{v:.4f}" if pd.notna(v) else "-" for v in row]) |
| + " \\\\" |
| ) |
| latex.append(row_str) |
|
|
| latex.append("\\bottomrule") |
| latex.append("\\end{tabular}") |
| latex.append("\\caption{LLM Share by Model and Project}") |
| latex.append("\\label{tab:llm_share}") |
| latex.append("\\end{table*}") |
|
|
| return "\n".join(latex) |
|
|
|
|
| if __name__ == "__main__": |
| aggregate_llm_share() |
|
|
