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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()
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