| from model_inference import * | |
| from config import result_parsers, dataset_files, max_tokens, icl_files | |
| from tqdm import tqdm | |
| import json | |
| import os | |
| models = [gemma2b, llama2_7b] | |
| tasks = ["poi_identification", "trajectory_region", "trajectory_trajectory", "direction_determination", "trajectory_anomaly_detection", "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/icl_{}.log".format(task), 'a') | |
| error_writer.write(model.model_path+'\n') | |
| result_parser = result_parsers[task] | |
| context_samples = open(icl_files[task]) | |
| prompt = "" | |
| for _i, sample in enumerate(context_samples.readlines()): | |
| sample = json.loads(sample) | |
| prompt += "{}{}\n".format(sample['Question'], sample['Answer']) | |
| 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(prompt+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() | |