| 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() |
|
|
