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| from llm.llm import LLM | |
| from prompt.constants import modeling_methods | |
| from input.problem import problem_input | |
| # from input.test_middle_result import problem_str, problem_analysis, selected_models, modeling_solution, modeling_solution, task_descriptions | |
| from agent.problem_analysis import ProblemAnalysis | |
| from agent.method_ranking import MethodRanking | |
| from agent.problem_modeling import ProblemModeling | |
| from agent.task_decompse import TaskDecompose | |
| from agent.task import Task | |
| from agent.create_charts import Chart | |
| from agent.coordinator import Coordinator | |
| from utils.utils import read_json_file, write_json_file, write_text_file, json_to_markdown | |
| from prompt.template import TASK_ANALYSIS_APPEND_PROMPT, TASK_FORMULAS_APPEND_PROMPT, TASK_MODELING_APPEND_PROMPT | |
| # from utils.convert_format import markdown_to_latex | |
| import os | |
| from datetime import datetime | |
| import shutil | |
| def run_batch(problem_path, config, name, dataset_path, output_path): | |
| # Initialize LLM | |
| llm = LLM(config['model_name']) | |
| # Get problem input | |
| problem_str, problem = problem_input(problem_path, llm) | |
| problem_type = "" | |
| # Initialize paper dictionary | |
| paper = {'tasks': []} | |
| paper['problem_background'] = problem['background'] | |
| paper['problem_requirement'] = problem['problem_requirement'] | |
| # Problem analysis | |
| problem_analysis = "" | |
| paper['problem_analysis'] = problem_analysis | |
| # Problem modeling | |
| modeling_solution = "" | |
| # Task decomposition | |
| td = TaskDecompose(llm, coo = False) | |
| task_descriptions = td.decompose_and_refine(problem_str, problem_analysis, modeling_solution, problem_type, config['tasknum']) | |
| # Analyze dependency | |
| with_code = len(problem['dataset_path']) > 0 | |
| if with_code: | |
| shutil.copytree(dataset_path, os.path.join(output_path,'code'), dirs_exist_ok=True) | |
| # Process tasks | |
| task = Task(llm, coo = False) | |
| mr = MethodRanking(llm) | |
| chart = Chart(llm) | |
| for id in range(1, len(task_descriptions)+1): | |
| code_template = open(os.path.join('data/actor_data/input/code_template','main{}.py'.format(id))).read() | |
| work_dir = os.path.join(output_path,'code') | |
| script_name = 'main{}.py'.format(id) | |
| task_description = task_descriptions[id - 1] | |
| task_analysis = task.analysis("", task_description) | |
| description_and_analysis = f'## Task Description\n{task_description}\n\n## Task Analysis\n{task_analysis}' | |
| top_modeling_methods = mr.top_methods(description_and_analysis, top_k=config['top_method_num']) | |
| task_formulas = task.formulas("", problem['data_description'], task_description, task_analysis, top_modeling_methods, round=config['task_formulas_round']) | |
| task_modeling = task.modeling("", problem['data_description'], task_description, task_analysis, task_formulas) | |
| if with_code: | |
| task_code, is_pass, execution_result = task.coding(problem['dataset_path'], problem['data_description'], problem['variable_description'], task_description, task_analysis, task_formulas, task_modeling, "", code_template, script_name, work_dir) | |
| task_result = task.result(task_description, task_analysis, task_formulas, task_modeling, execution_result) | |
| task_answer = task.answer(task_description, task_analysis, task_formulas, task_modeling, task_result) | |
| task_dict = { | |
| 'task_description': task_description, | |
| 'task_analysis': task_analysis, | |
| 'preliminary_formulas': task_formulas, | |
| 'mathematical_modeling_process': task_modeling, | |
| 'task_code': task_code, | |
| 'is_pass': is_pass, | |
| 'execution_result': execution_result, | |
| 'solution_interpretation': task_result, | |
| 'subtask_outcome_analysis': task_answer | |
| } | |
| else: | |
| task_result = task.result(task_description, task_analysis, task_formulas, task_modeling) | |
| task_answer = task.answer(task_description, task_analysis, task_formulas, task_modeling, task_result) | |
| task_dict = { | |
| 'task_description': task_description, | |
| 'task_analysis': task_analysis, | |
| 'preliminary_formulas': task_formulas, | |
| 'mathematical_modeling_process': task_modeling, | |
| 'solution_interpretation': task_result, | |
| 'subtask_outcome_analysis': task_answer | |
| } | |
| charts = chart.create_charts(str(task_dict), config['chart_num']) | |
| task_dict['charts'] = charts | |
| paper['tasks'].append(task_dict) | |
| save_paper(paper, name, output_path) | |
| print(paper) | |
| print('Usage:', llm.get_total_usage()) | |
| write_json_file(f'{output_path}/usage/{name}.json', llm.get_total_usage()) | |
| return paper | |
| def save_paper(paper, name, path): | |
| write_json_file(f'{path}/json/{name}.json', paper) | |
| markdown_str = json_to_markdown(paper) | |
| write_text_file(f'{path}/markdown/{name}.md', markdown_str) | |
| # write_text_file(f'data/actor_data/output/latex/{name}.tex', markdown_to_latex(markdown_str)) | |
| def mkdir(path): | |
| os.mkdir(path) | |
| os.mkdir(path + '/json') | |
| os.mkdir(path + '/markdown') | |
| os.mkdir(path + '/latex') | |
| os.mkdir(path + '/code') | |
| os.mkdir(path + '/usage') | |
| if __name__ == "__main__": | |
| import glob | |
| # files = glob.glob('data/actor_data/input/problem/2024*') | |
| files = glob.glob('data/actor_data/input/problem/2024_C*') | |
| # config_list = [{ | |
| # 'top_method_num': 6, | |
| # 'problem_analysis_round': 1, | |
| # 'problem_modeling_round': 1, | |
| # 'task_formulas_round': 1, | |
| # 'tasknum': 4, | |
| # 'chart_num': 3, | |
| # 'model_name': 'gpt-4' | |
| # # 'model_name': 'chatgpt-4o-latest' | |
| # }, { | |
| # 'top_method_num': 6, | |
| # 'problem_analysis_round': 1, | |
| # 'problem_modeling_round': 1, | |
| # 'task_formulas_round': 1, | |
| # 'tasknum': 4, | |
| # 'chart_num': 3, | |
| # 'model_name': 'deepseek-reasoner' | |
| # }, { | |
| # 'top_method_num': 6, | |
| # 'problem_analysis_round': 1, | |
| # 'problem_modeling_round': 1, | |
| # 'task_formulas_round': 1, | |
| # 'tasknum': 4, | |
| # 'chart_num': 3, | |
| # 'model_name': 'DeepSeek-R1-671B' | |
| # }][0:] | |
| config_list = [{ | |
| 'top_method_num': 6, | |
| 'problem_analysis_round': 1, | |
| 'problem_modeling_round': 1, | |
| 'task_formulas_round': 1, | |
| 'tasknum': 4, | |
| 'chart_num': 3, | |
| 'model_name': 'gpt-4' | |
| }][0:] | |
| for i, config in enumerate(config_list, start=1): | |
| for file in files: | |
| try: | |
| name = file.split('/')[-1].split('.')[0] + '_wo_coo' | |
| dataset_path = os.path.join('data/actor_data/input/dataset', file.split('/')[-1].split('.')[0]) | |
| output_dir = 'data/actor_data/output' | |
| if not os.path.exists(output_dir): | |
| os.makedirs(output_dir) | |
| output_path = os.path.join(output_dir, name + '_{}'.format(datetime.now().strftime('%Y%m%d-%H%M%S'))) | |
| if not os.path.exists(output_path): | |
| mkdir(output_path) | |
| print(f'Processing {file}..., config: {config}') | |
| paper = run_batch(problem_path=file, config=config, name=name, dataset_path=dataset_path, output_path=output_path) | |
| # save_paper(paper, name) | |
| except Exception as e: | |
| raise | |
| print(f'Error: {e}') | |
| continue | |