| from model_inference import * |
| from config import result_parsers, dataset_files, max_tokens |
| from tqdm import tqdm |
| import json |
| import os |
|
|
| models = [chatglm2, chatglm3, deepseek7b, falcon7b, gemma2b, gemma7b, llama2_7b, mistral7b, phi2, qwen7b, vicuna7b, yi6b, chatgpt, gpt4o] |
| tasks = ["poi_category_recognition", "poi_identification", "urban_region_function_recognition", "administrative_region_determination", "point_trajectory", "point_region", "trajectory_region", "trajectory_identification", "trajectory_trajectory", "direction_determination", "trajectory_anomaly_detection", "trajectory_classification", "trajectory_prediction"] |
|
|
| if not os.path.exists("./logs"): |
| os.mkdir("./logs") |
|
|
| for fun in models: |
| model = fun() |
| for task in tasks: |
| error_writer = open("./logs/{}.log".format(task), 'a') |
| error_writer.write(model.model_path+'\n') |
| result_parser = result_parsers[task] |
| for dataset_path in dataset_files[task]: |
| dataset = open(dataset_path, 'r') |
| dataset = dataset.readlines() |
|
|
| correct = 0 |
| total = 0 |
| exception = 0 |
|
|
| for i, item in tqdm(enumerate(dataset), total=len(dataset)): |
| item = json.loads(item) |
| response = model.generate(item["Question"], max_tokens[task]) |
| score = result_parser(response, item["Answer"], error_writer) |
| |
| if task!='trajectory_prediction' or score is not None: |
| total +=1 |
| if score is None: |
| exception += 1 |
| else: |
| correct += score |
|
|
| if i%100==0: |
| print("Dataset: {}\nTotal: {}, correct:{}, exception:{}, accuracy:{}\n\n".format(dataset_path, total, correct, exception, correct/total)) |
| |
| error_writer.write("Dataset: {}\nTotal: {}, correct:{}, exception:{}, accuracy:{}\n\n".format(dataset_path, total, correct, exception, correct/total)) |
| error_writer.flush() |
| error_writer.write("\n") |
| error_writer.close() |
|
|
