import numpy as np import torch from get_data import get_sst2 import os import random from utils import generate_uploader, generate_user, TextDataLoader, train, eval_prediction from learnware.learnware import Learnware from learnware.reuse import JobSelectorReuser, AveragingReuser, EnsemblePruningReuser import time import pickle from learnware.market import instantiate_learnware_market, BaseUserInfo from learnware.specification import RKMETextSpecification from learnware.logger import get_module_logger from shutil import copyfile, rmtree import zipfile logger = get_module_logger("text_test", level="INFO") origin_data_root = "./data/origin_data" processed_data_root = "./data/processed_data" tmp_dir = "./data/tmp" learnware_pool_dir = "./data/learnware_pool" dataset = "sst2" n_uploaders = 10 n_users = 5 n_classes = 2 data_root = os.path.join(origin_data_root, dataset) data_save_root = os.path.join(processed_data_root, dataset) user_save_root = os.path.join(data_save_root, "user") uploader_save_root = os.path.join(data_save_root, "uploader") model_save_root = os.path.join(data_save_root, "uploader_model") os.makedirs(data_root, exist_ok=True) os.makedirs(user_save_root, exist_ok=True) os.makedirs(uploader_save_root, exist_ok=True) os.makedirs(model_save_root, exist_ok=True) output_description = { "Dimension": 2, "Description": { "0": "the probability of being negative", "1": "the probability of being positive", }, } semantic_specs = [ { "Data": {"Values": ["Text"], "Type": "Class"}, "Task": {"Values": ["Classification"], "Type": "Class"}, "Library": {"Values": ["PyTorch"], "Type": "Class"}, "Scenario": {"Values": ["Business"], "Type": "Tag"}, "Description": {"Values": "", "Type": "String"}, "Name": {"Values": "learnware_1", "Type": "String"}, "Output": output_description, } ] user_semantic = { "Data": {"Values": ["Text"], "Type": "Class"}, "Task": {"Values": ["Classification"], "Type": "Class"}, "Library": {"Values": ["PyTorch"], "Type": "Class"}, "Scenario": {"Values": ["Business"], "Type": "Tag"}, "Description": {"Values": "", "Type": "String"}, "Name": {"Values": "", "Type": "String"}, "Output": output_description, } def prepare_data(): if dataset == "sst2": X_train, y_train, X_test, y_test = get_sst2(data_root) else: return generate_uploader(X_train, y_train, n_uploaders=n_uploaders, data_save_root=uploader_save_root) generate_user(X_test, y_test, n_users=n_users, data_save_root=user_save_root) def prepare_model(): dataloader = TextDataLoader(data_save_root, train=True) for i in range(n_uploaders): logger.info("Train on uploader: %d" % (i)) X, y = dataloader.get_idx_data(i) model = train(X, y, out_classes=n_classes) model_save_path = os.path.join(model_save_root, "uploader_%d.pth" % (i)) torch.save(model.state_dict(), model_save_path) logger.info("Model saved to '%s'" % (model_save_path)) def prepare_learnware(data_path, model_path, init_file_path, yaml_path, env_file_path, save_root, zip_name): os.makedirs(save_root, exist_ok=True) tmp_spec_path = os.path.join(save_root, "rkme.json") tmp_model_path = os.path.join(save_root, "model.pth") tmp_yaml_path = os.path.join(save_root, "learnware.yaml") tmp_init_path = os.path.join(save_root, "__init__.py") tmp_env_path = os.path.join(save_root, "requirements.txt") with open(data_path, "rb") as f: X = pickle.load(f) semantic_spec = semantic_specs[0] st = time.time() user_spec = RKMETextSpecification() user_spec.generate_stat_spec_from_data(X=X) ed = time.time() logger.info("Stat spec generated in %.3f s" % (ed - st)) user_spec.save(tmp_spec_path) copyfile(model_path, tmp_model_path) copyfile(yaml_path, tmp_yaml_path) copyfile(init_file_path, tmp_init_path) copyfile(env_file_path, tmp_env_path) zip_file_name = os.path.join(learnware_pool_dir, "%s.zip" % (zip_name)) with zipfile.ZipFile(zip_file_name, "w", compression=zipfile.ZIP_DEFLATED) as zip_obj: zip_obj.write(tmp_spec_path, "rkme.json") zip_obj.write(tmp_model_path, "model.pth") zip_obj.write(tmp_yaml_path, "learnware.yaml") zip_obj.write(tmp_init_path, "__init__.py") zip_obj.write(tmp_env_path, "requirements.txt") rmtree(save_root) logger.info("New Learnware Saved to %s" % (zip_file_name)) return zip_file_name def prepare_market(): text_market = instantiate_learnware_market(market_id="sst2", rebuild=True) try: rmtree(learnware_pool_dir) except: pass os.makedirs(learnware_pool_dir, exist_ok=True) for i in range(n_uploaders): data_path = os.path.join(uploader_save_root, "uploader_%d_X.pkl" % (i)) model_path = os.path.join(model_save_root, "uploader_%d.pth" % (i)) init_file_path = "./example_files/example_init.py" yaml_file_path = "./example_files/example_yaml.yaml" env_file_path = "./example_files/requirements.txt" new_learnware_path = prepare_learnware( data_path, model_path, init_file_path, yaml_file_path, env_file_path, tmp_dir, "%s_%d" % (dataset, i) ) semantic_spec = semantic_specs[0] semantic_spec["Name"]["Values"] = "learnware_%d" % (i) semantic_spec["Description"]["Values"] = "test_learnware_number_%d" % (i) text_market.add_learnware(new_learnware_path, semantic_spec) logger.info("Total Item: %d" % (len(text_market))) def test_search(gamma=0.1, load_market=True): if load_market: text_market = instantiate_learnware_market(market_id="sst2") else: prepare_market() text_market = instantiate_learnware_market(market_id="sst2") logger.info("Number of items in the market: %d" % len(text_market)) select_list = [] avg_list = [] improve_list = [] job_selector_score_list = [] ensemble_score_list = [] pruning_score_list = [] for i in range(n_users): user_data_path = os.path.join(user_save_root, "user_%d_X.pkl" % (i)) user_label_path = os.path.join(user_save_root, "user_%d_y.pkl" % (i)) with open(user_data_path, "rb") as f: user_data = pickle.load(f) with open(user_label_path, "rb") as f: user_label = pickle.load(f) user_stat_spec = RKMETextSpecification() user_stat_spec.generate_stat_spec_from_data(X=user_data) user_info = BaseUserInfo(semantic_spec=user_semantic, stat_info={"RKMETextSpecification": user_stat_spec}) logger.info("Searching Market for user: %d" % (i)) sorted_score_list, single_learnware_list, mixture_score, mixture_learnware_list = text_market.search_learnware( user_info ) l = len(sorted_score_list) acc_list = [] for idx in range(l): learnware = single_learnware_list[idx] score = sorted_score_list[idx] pred_y = learnware.predict(user_data) acc = eval_prediction(pred_y, user_label) acc_list.append(acc) logger.info("search rank: %d, score: %.3f, learnware_id: %s, acc: %.3f" % (idx, score, learnware.id, acc)) # test reuse (job selector) reuse_baseline = JobSelectorReuser(learnware_list=mixture_learnware_list, herding_num=100) reuse_predict = reuse_baseline.predict(user_data=user_data) reuse_score = eval_prediction(reuse_predict, user_label) job_selector_score_list.append(reuse_score) print(f"mixture reuse loss(job selector): {reuse_score}") # test reuse (ensemble) reuse_ensemble = AveragingReuser(learnware_list=mixture_learnware_list, mode="vote_by_label") ensemble_predict_y = reuse_ensemble.predict(user_data=user_data) ensemble_score = eval_prediction(ensemble_predict_y, user_label) ensemble_score_list.append(ensemble_score) print(f"mixture reuse accuracy (ensemble): {ensemble_score}") select_list.append(acc_list[0]) avg_list.append(np.mean(acc_list)) improve_list.append((acc_list[0] - np.mean(acc_list)) / np.mean(acc_list)) # test reuse (ensemblePruning) reuse_pruning = EnsemblePruningReuser(learnware_list=mixture_learnware_list) pruning_predict_y = reuse_pruning.predict(user_data=user_data) pruning_score = eval_prediction(pruning_predict_y, user_label) pruning_score_list.append(pruning_score) print(f"mixture reuse accuracy (ensemble Pruning): {pruning_score}\n") select_list.append(acc_list[0]) avg_list.append(np.mean(acc_list)) improve_list.append((acc_list[0] - np.mean(acc_list)) / np.mean(acc_list)) logger.info( "Accuracy of selected learnware: %.3f +/- %.3f, Average performance: %.3f +/- %.3f" % (np.mean(select_list), np.std(select_list), np.mean(avg_list), np.std(avg_list)) ) logger.info("Average performance improvement: %.3f" % (np.mean(improve_list))) logger.info( "Average Job Selector Reuse Performance: %.3f +/- %.3f" % (np.mean(job_selector_score_list), np.std(job_selector_score_list)) ) logger.info( "Averaging Ensemble Reuse Performance: %.3f +/- %.3f" % (np.mean(ensemble_score_list), np.std(ensemble_score_list)) ) logger.info( "Selective Ensemble Reuse Performance: %.3f +/- %.3f" % (np.mean(pruning_score_list), np.std(pruning_score_list)) ) if __name__ == "__main__": prepare_data() prepare_model() test_search(load_market=False)