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import torch |
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import unittest |
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import os |
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import copy |
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import joblib |
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import zipfile |
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import numpy as np |
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import multiprocessing |
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from sklearn.linear_model import Ridge |
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from sklearn.datasets import make_regression |
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from shutil import copyfile, rmtree |
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from learnware.client import LearnwareClient |
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from sklearn.metrics import mean_squared_error |
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import learnware |
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from learnware.market import instantiate_learnware_market, BaseUserInfo |
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from learnware.specification import RKMETableSpecification, generate_rkme_table_spec |
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from learnware.reuse import HeteroMapAlignLearnware, AveragingReuser, EnsemblePruningReuser |
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from example_learnwares.config import ( |
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input_shape_list, |
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input_description_list, |
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output_description_list, |
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user_description_list, |
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) |
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curr_root = os.path.dirname(os.path.abspath(__file__)) |
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user_semantic = { |
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"Data": {"Values": ["Table"], "Type": "Class"}, |
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"Task": { |
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"Values": ["Regression"], |
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"Type": "Class", |
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}, |
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"Library": {"Values": ["Scikit-learn"], "Type": "Class"}, |
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"Scenario": {"Values": ["Education"], "Type": "Tag"}, |
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"Description": {"Values": "", "Type": "String"}, |
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"Name": {"Values": "", "Type": "String"}, |
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"License": {"Values": ["MIT"], "Type": "Class"}, |
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} |
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def check_learnware(learnware_name, dir_path=os.path.join(curr_root, "learnware_pool")): |
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print(f"Checking Learnware: {learnware_name}") |
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zip_file_path = os.path.join(dir_path, learnware_name) |
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client = LearnwareClient() |
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# if check_learnware doesn't raise an exception, return True, otherwise, return false |
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try: |
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client.check_learnware(zip_file_path) |
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return True |
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except Exception as e: |
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print(f"Learnware {learnware_name} failed the check: {e}") |
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return False |
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class TestMarket(unittest.TestCase): |
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@classmethod |
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def setUpClass(cls) -> None: |
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np.random.seed(2023) |
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learnware.init() |
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def _init_learnware_market(self, organizer_kwargs=None): |
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"""initialize learnware market""" |
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hetero_market = instantiate_learnware_market( |
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market_id="hetero_toy", name="hetero", rebuild=True, organizer_kwargs=organizer_kwargs |
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) |
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return hetero_market |
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def test_prepare_learnware_randomly(self, learnware_num=5): |
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self.zip_path_list = [] |
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for i in range(learnware_num): |
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dir_path = os.path.join(curr_root, "learnware_pool", "ridge_%d" % (i)) |
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os.makedirs(dir_path, exist_ok=True) |
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print("Preparing Learnware: %d" % (i)) |
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example_learnware_idx = i % 2 |
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input_dim = input_shape_list[example_learnware_idx] |
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learnware_example_dir = "example_learnwares" |
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X, y = make_regression(n_samples=5000, n_informative=15, n_features=input_dim, noise=0.1, random_state=42) |
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clf = Ridge(alpha=1.0) |
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clf.fit(X, y) |
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joblib.dump(clf, os.path.join(dir_path, "ridge.pkl")) |
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spec = generate_rkme_table_spec(X=X, gamma=0.1, cuda_idx=0) |
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spec.save(os.path.join(dir_path, "stat.json")) |
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init_file = os.path.join(dir_path, "__init__.py") |
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copyfile( |
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os.path.join(curr_root, learnware_example_dir, f"model{example_learnware_idx}.py"), init_file |
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) # cp example_init.py init_file |
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yaml_file = os.path.join(dir_path, "learnware.yaml") |
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copyfile( |
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os.path.join(curr_root, learnware_example_dir, "learnware.yaml"), yaml_file |
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) # cp example.yaml yaml_file |
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env_file = os.path.join(dir_path, "requirements.txt") |
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copyfile(os.path.join(curr_root, learnware_example_dir, "requirements.txt"), env_file) |
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zip_file = dir_path + ".zip" |
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# zip -q -r -j zip_file dir_path |
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with zipfile.ZipFile(zip_file, "w") as zip_obj: |
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for foldername, subfolders, filenames in os.walk(dir_path): |
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for filename in filenames: |
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file_path = os.path.join(foldername, filename) |
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zip_info = zipfile.ZipInfo(filename) |
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zip_info.compress_type = zipfile.ZIP_STORED |
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with open(file_path, "rb") as file: |
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zip_obj.writestr(zip_info, file.read()) |
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rmtree(dir_path) # rm -r dir_path |
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self.zip_path_list.append(zip_file) |
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def test_generated_learnwares(self): |
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curr_root = os.path.dirname(os.path.abspath(__file__)) |
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dir_path = os.path.join(curr_root, "learnware_pool") |
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# Execute multi-process checking using Pool |
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mp_context = multiprocessing.get_context("spawn") |
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with mp_context.Pool() as pool: |
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results = pool.starmap(check_learnware, [(name, dir_path) for name in os.listdir(dir_path)]) |
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# Use an assert statement to ensure that all checks return True |
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self.assertTrue(all(results), "Not all learnwares passed the check") |
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def test_upload_delete_learnware(self, learnware_num=5, delete=True): |
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hetero_market = self._init_learnware_market() |
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self.test_prepare_learnware_randomly(learnware_num) |
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self.learnware_num = learnware_num |
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print("Total Item:", len(hetero_market)) |
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assert len(hetero_market) == 0, f"The market should be empty!" |
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for idx, zip_path in enumerate(self.zip_path_list): |
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semantic_spec = copy.deepcopy(user_semantic) |
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semantic_spec["Name"]["Values"] = "learnware_%d" % (idx) |
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semantic_spec["Description"]["Values"] = "test_learnware_number_%d" % (idx) |
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semantic_spec["Input"] = input_description_list[idx % 2] |
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semantic_spec["Output"] = output_description_list[idx % 2] |
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hetero_market.add_learnware(zip_path, semantic_spec) |
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print("Total Item:", len(hetero_market)) |
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assert len(hetero_market) == self.learnware_num, f"The number of learnwares must be {self.learnware_num}!" |
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curr_inds = hetero_market.get_learnware_ids() |
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print("Available ids After Uploading Learnwares:", curr_inds) |
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assert len(curr_inds) == self.learnware_num, f"The number of learnwares must be {self.learnware_num}!" |
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if delete: |
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for learnware_id in curr_inds: |
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hetero_market.delete_learnware(learnware_id) |
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self.learnware_num -= 1 |
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assert ( |
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len(hetero_market) == self.learnware_num |
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), f"The number of learnwares must be {self.learnware_num}!" |
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curr_inds = hetero_market.get_learnware_ids() |
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print("Available ids After Deleting Learnwares:", curr_inds) |
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assert len(curr_inds) == 0, f"The market should be empty!" |
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return hetero_market |
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def test_train_market_model(self, learnware_num=5): |
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hetero_market = self._init_learnware_market( |
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organizer_kwargs={"auto_update": False, "auto_update_limit": learnware_num} |
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) |
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self.test_prepare_learnware_randomly(learnware_num) |
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self.learnware_num = learnware_num |
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print("Total Item:", len(hetero_market)) |
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assert len(hetero_market) == 0, f"The market should be empty!" |
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for idx, zip_path in enumerate(self.zip_path_list): |
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semantic_spec = copy.deepcopy(user_semantic) |
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semantic_spec["Name"]["Values"] = "learnware_%d" % (idx) |
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semantic_spec["Description"]["Values"] = "test_learnware_number_%d" % (idx) |
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semantic_spec["Input"] = input_description_list[idx % 2] |
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semantic_spec["Output"] = output_description_list[idx % 2] |
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hetero_market.add_learnware(zip_path, semantic_spec) |
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print("Total Item:", len(hetero_market)) |
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assert len(hetero_market) == self.learnware_num, f"The number of learnwares must be {self.learnware_num}!" |
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curr_inds = hetero_market.get_learnware_ids() |
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print("Available ids After Uploading Learnwares:", curr_inds) |
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assert len(curr_inds) == self.learnware_num, f"The number of learnwares must be {self.learnware_num}!" |
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# organizer=hetero_market.learnware_organizer |
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# organizer.train(hetero_market.learnware_organizer.learnware_list.values()) |
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return hetero_market |
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def test_search_semantics(self, learnware_num=5): |
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hetero_market = self.test_upload_delete_learnware(learnware_num, delete=False) |
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print("Total Item:", len(hetero_market)) |
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assert len(hetero_market) == self.learnware_num, f"The number of learnwares must be {self.learnware_num}!" |
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semantic_spec = copy.deepcopy(user_semantic) |
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semantic_spec["Name"]["Values"] = f"learnware_{learnware_num - 1}" |
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user_info = BaseUserInfo(semantic_spec=semantic_spec) |
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search_result = hetero_market.search_learnware(user_info) |
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single_result = search_result.get_single_results() |
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print("User info:", user_info.get_semantic_spec()) |
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print(f"Search result:") |
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assert len(single_result) == 1, f"Exact semantic search failed!" |
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for search_item in single_result: |
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semantic_spec1 = search_item.learnware.get_specification().get_semantic_spec() |
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print("Choose learnware:", search_item.learnware.id, semantic_spec1) |
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assert semantic_spec1["Name"]["Values"] == semantic_spec["Name"]["Values"], f"Exact semantic search failed!" |
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semantic_spec["Name"]["Values"] = "laernwaer" |
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user_info = BaseUserInfo(semantic_spec=semantic_spec) |
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search_result = hetero_market.search_learnware(user_info) |
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single_result = search_result.get_single_results() |
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print("User info:", user_info.get_semantic_spec()) |
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print(f"Search result:") |
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assert len(single_result) == self.learnware_num, f"Fuzzy semantic search failed!" |
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for search_item in single_result: |
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semantic_spec1 = search_item.learnware.get_specification().get_semantic_spec() |
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print("Choose learnware:", search_item.learnware.id, semantic_spec1) |
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def test_stat_search(self, learnware_num=5): |
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hetero_market = self.test_train_market_model(learnware_num) |
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print("Total Item:", len(hetero_market)) |
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# hetero test |
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print("+++++ HETERO TEST ++++++") |
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user_dim = 15 |
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test_folder = os.path.join(curr_root, "test_stat") |
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for idx, zip_path in enumerate(self.zip_path_list): |
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unzip_dir = os.path.join(test_folder, f"{idx}") |
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# unzip -o -q zip_path -d unzip_dir |
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if os.path.exists(unzip_dir): |
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rmtree(unzip_dir) |
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os.makedirs(unzip_dir, exist_ok=True) |
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with zipfile.ZipFile(zip_path, "r") as zip_obj: |
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zip_obj.extractall(path=unzip_dir) |
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user_spec = RKMETableSpecification() |
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user_spec.load(os.path.join(unzip_dir, "stat.json")) |
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z = user_spec.get_z() |
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z = z[:, :user_dim] |
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device = user_spec.device |
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z = torch.tensor(z, device=device) |
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user_spec.z = z |
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print(">> normal case test:") |
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semantic_spec = copy.deepcopy(user_semantic) |
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semantic_spec["Input"] = copy.deepcopy(input_description_list[idx % 2]) |
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semantic_spec["Input"]["Dimension"] = user_dim |
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# keep only the first user_dim descriptions |
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semantic_spec["Input"]["Description"] = { |
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str(key): semantic_spec["Input"]["Description"][str(key)] for key in range(user_dim) |
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} |
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user_info = BaseUserInfo(semantic_spec=semantic_spec, stat_info={"RKMETableSpecification": user_spec}) |
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search_result = hetero_market.search_learnware(user_info) |
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single_result = search_result.get_single_results() |
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multiple_result = search_result.get_multiple_results() |
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print(f"search result of user{idx}:") |
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for single_item in single_result: |
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print(f"score: {single_item.score}, learnware_id: {single_item.learnware.id}") |
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for multiple_item in multiple_result: |
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print( |
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f"mixture_score: {multiple_item.score}, mixture_learnware_ids: {[item.id for item in multiple_item.learnwares]}" |
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) |
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# inproper key "Task" in semantic_spec, use homo search and print invalid semantic_spec |
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print(">> test for key 'Task' has empty 'Values':") |
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semantic_spec["Task"] = {"Values": ["Segmentation"], "Type": "Class"} |
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user_info = BaseUserInfo(semantic_spec=semantic_spec, stat_info={"RKMETableSpecification": user_spec}) |
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search_result = hetero_market.search_learnware(user_info) |
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single_result = search_result.get_single_results() |
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assert len(single_result) == 0, f"Statistical search failed!" |
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# delete key "Task" in semantic_spec, use homo search and print WARNING INFO with "User doesn't provide correct task type" |
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print(">> delele key 'Task' test:") |
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semantic_spec.pop("Task") |
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user_info = BaseUserInfo(semantic_spec=semantic_spec, stat_info={"RKMETableSpecification": user_spec}) |
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search_result = hetero_market.search_learnware(user_info) |
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single_result = search_result.get_single_results() |
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assert len(single_result) == 0, f"Statistical search failed!" |
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# modify semantic info with mismatch dim, use homo search and print "User data feature dimensions mismatch with semantic specification." |
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print(">> mismatch dim test") |
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semantic_spec = copy.deepcopy(user_semantic) |
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semantic_spec["Input"] = copy.deepcopy(input_description_list[idx % 2]) |
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semantic_spec["Input"]["Dimension"] = user_dim - 2 |
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semantic_spec["Input"]["Description"] = { |
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str(key): semantic_spec["Input"]["Description"][str(key)] for key in range(user_dim) |
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} |
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user_info = BaseUserInfo(semantic_spec=semantic_spec, stat_info={"RKMETableSpecification": user_spec}) |
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search_result = hetero_market.search_learnware(user_info) |
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single_result = search_result.get_single_results() |
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assert len(single_result) == 0, f"Statistical search failed!" |
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rmtree(test_folder) # rm -r test_folder |
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# homo test |
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print("\n+++++ HOMO TEST ++++++") |
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test_folder = os.path.join(curr_root, "test_stat") |
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for idx, zip_path in enumerate(self.zip_path_list): |
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unzip_dir = os.path.join(test_folder, f"{idx}") |
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# unzip -o -q zip_path -d unzip_dir |
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if os.path.exists(unzip_dir): |
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rmtree(unzip_dir) |
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os.makedirs(unzip_dir, exist_ok=True) |
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with zipfile.ZipFile(zip_path, "r") as zip_obj: |
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zip_obj.extractall(path=unzip_dir) |
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user_spec = RKMETableSpecification() |
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user_spec.load(os.path.join(unzip_dir, "stat.json")) |
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user_info = BaseUserInfo(semantic_spec=user_semantic, stat_info={"RKMETableSpecification": user_spec}) |
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search_result = hetero_market.search_learnware(user_info) |
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single_result = search_result.get_single_results() |
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multiple_result = search_result.get_multiple_results() |
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assert len(single_result) >= 1, f"Statistical search failed!" |
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print(f"search result of user{idx}:") |
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for single_item in single_result: |
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print(f"score: {single_item.score}, learnware_id: {single_item.learnware.id}") |
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for multiple_item in multiple_result: |
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print(f"mixture_score: {multiple_item.score}\n") |
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mixture_id = " ".join([learnware.id for learnware in multiple_item.learnwares]) |
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print(f"mixture_learnware: {mixture_id}\n") |
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rmtree(test_folder) # rm -r test_folder |
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def test_model_reuse(self, learnware_num=5): |
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# generate toy regression problem |
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X, y = make_regression(n_samples=5000, n_informative=10, n_features=15, noise=0.1, random_state=0) |
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# generate rkme |
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user_spec = generate_rkme_table_spec(X=X, gamma=0.1, cuda_idx=0) |
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# generate specification |
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semantic_spec = copy.deepcopy(user_semantic) |
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semantic_spec["Input"] = user_description_list[0] |
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user_info = BaseUserInfo(semantic_spec=semantic_spec, stat_info={"RKMETableSpecification": user_spec}) |
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# learnware market search |
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hetero_market = self.test_train_market_model(learnware_num) |
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search_result = hetero_market.search_learnware(user_info) |
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single_result = search_result.get_single_results() |
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multiple_result = search_result.get_multiple_results() |
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# print search results |
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for single_item in single_result: |
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print(f"score: {single_item.score}, learnware_id: {single_item.learnware.id}") |
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for multiple_item in multiple_result: |
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print( |
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f"mixture_score: {multiple_item.score}, mixture_learnware_ids: {[item.id for item in multiple_item.learnwares]}" |
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) |
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# single model reuse |
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hetero_learnware = HeteroMapAlignLearnware(single_result[0].learnware, mode="regression") |
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hetero_learnware.align(user_spec, X[:100], y[:100]) |
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single_predict_y = hetero_learnware.predict(X) |
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# multi model reuse |
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hetero_learnware_list = [] |
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for learnware in multiple_result[0].learnwares: |
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hetero_learnware = HeteroMapAlignLearnware(learnware, mode="regression") |
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hetero_learnware.align(user_spec, X[:100], y[:100]) |
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hetero_learnware_list.append(hetero_learnware) |
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# Use averaging ensemble reuser to reuse the searched learnwares to make prediction |
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reuse_ensemble = AveragingReuser(learnware_list=hetero_learnware_list, mode="mean") |
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ensemble_predict_y = reuse_ensemble.predict(user_data=X) |
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# Use ensemble pruning reuser to reuse the searched learnwares to make prediction |
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reuse_ensemble = EnsemblePruningReuser(learnware_list=hetero_learnware_list, mode="regression") |
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reuse_ensemble.fit(X[:100], y[:100]) |
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ensemble_pruning_predict_y = reuse_ensemble.predict(user_data=X) |
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print("Single model RMSE by finetune:", mean_squared_error(y, single_predict_y, squared=False)) |
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print("Averaging Reuser RMSE:", mean_squared_error(y, ensemble_predict_y, squared=False)) |
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print("Ensemble Pruning Reuser RMSE:", mean_squared_error(y, ensemble_pruning_predict_y, squared=False)) |
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def suite(): |
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_suite = unittest.TestSuite() |
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_suite.addTest(TestMarket("test_prepare_learnware_randomly")) |
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_suite.addTest(TestMarket("test_generated_learnwares")) |
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_suite.addTest(TestMarket("test_upload_delete_learnware")) |
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_suite.addTest(TestMarket("test_train_market_model")) |
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_suite.addTest(TestMarket("test_search_semantics")) |
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_suite.addTest(TestMarket("test_stat_search")) |
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_suite.addTest(TestMarket("test_model_reuse")) |
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return _suite |
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if __name__ == "__main__": |
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|
runner = unittest.TextTestRunner() |
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runner.run(suite()) |
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bxdd
1 year ago