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LE|UCv-EaQ^HE2Mz diff --git a/docs/start/exp.rst b/docs/start/exp.rst index 50d4574..34db05e 100644 --- a/docs/start/exp.rst +++ b/docs/start/exp.rst @@ -46,21 +46,21 @@ Based on the specific design of user tasks, our experiments were primarily categ Homo Experiments ----------------------- -For homogeneous experiments, the 55 stores in the Corporacion dataset act as 55 users, each applying one feature engineering method, -and using the test data from their respective store as user data. These users can then search for homogeneous learnwares in the market with the same feature spaces as their tasks. +In homogeneous experiments, the 55 stores in the Corporacion dataset are considered as 55 users. Each store uses the same feature engineering method +and their own test set as user data. These users then search for and reuse homogeneous learnwares in the market which exactly match the feature spaces of their tasks. The Mean Squared Error (MSE) of search and reuse is presented in the table below: +-----------------------------------+---------------------+ -| Mean in Market (Single) | 0.331 ± 0.040 | +| Mean in Market (Single) | 0.323 ± 0.041 | +-----------------------------------+---------------------+ -| Best in Market (Single) | 0.151 ± 0.046 | +| Best in Market (Single) | 0.302 ± 0.036 | +-----------------------------------+---------------------+ -| Top-1 Reuse (Single) | 0.280 ± 0.090 | +| Top-1 Reuse (Single) | 0.307 ± 0.037 | +-----------------------------------+---------------------+ -| Job Selector Reuse (Multiple) | 0.274 ± 0.064 | +| Job Selector Reuse (Multiple) | 0.308 ± 0.038 | +-----------------------------------+---------------------+ -| Average Ensemble Reuse (Multiple) | 0.267 ± 0.051 | +| Average Ensemble Reuse (Multiple) | 0.304 ± 0.036 | +-----------------------------------+---------------------+ When users have both test data and limited training data derived from their original data, reusing single or multiple searched learnwares from the market can often yield @@ -72,8 +72,8 @@ The average results across 55 users are depicted in the figure below: :align: center :alt: Table Homo Limited Labeled Data -From the figure, it's evident that when users have limited training data, the performance of reusing single/multiple table learnwares is superior to that of the user's own model. -This emphasizes the benefit of learnware reuse in significantly reducing the need for extensive training data and achieving enhanced results when available user training data is limited. +The figure clearly shows that when users have limited training data, reusing single or multiple table learnwares outperforms the user's own model. +This highlights the advantage of reusing learnwares in substantially reducing the need for large training datasets and achieving better outcomes with restricted user training data. Hetero Experiments From 9ceb5259540a0467cfb630521b8ae15736286708 Mon Sep 17 00:00:00 2001 From: liuht Date: Mon, 25 Dec 2023 17:29:47 +0800 Subject: [PATCH 02/32] [ENH] add dataset_table_workflow --- examples/base.py | 199 ++++++++++++++++++ examples/dataset_table_workflow/config.py | 65 ++++++ examples/dataset_table_workflow/hetero.py | 225 +++++++++++++++++++++ examples/dataset_table_workflow/homo.py | 204 +++++++++++++++++++ examples/dataset_table_workflow/main.py | 34 ++++ examples/dataset_table_workflow/methods.py | 105 ++++++++++ examples/dataset_table_workflow/utils.py | 109 ++++++++++ 7 files changed, 941 insertions(+) create mode 100644 examples/base.py create mode 100644 examples/dataset_table_workflow/config.py create mode 100644 examples/dataset_table_workflow/hetero.py create mode 100644 examples/dataset_table_workflow/homo.py create mode 100644 examples/dataset_table_workflow/main.py create mode 100644 examples/dataset_table_workflow/methods.py create mode 100644 examples/dataset_table_workflow/utils.py diff --git a/examples/base.py b/examples/base.py new file mode 100644 index 0000000..4dfd4fb --- /dev/null +++ b/examples/base.py @@ -0,0 +1,199 @@ +import os +import joblib +import zipfile +from shutil import copyfile, rmtree + +import json +from learnware.client import LearnwareClient +from learnware.logger import get_module_logger +from learnware.market import instantiate_learnware_market +from multiprocessing import Pool + +from benchmarks import DataLoader +from config import * +from methods import * +from utils import process_single_aug + +logger = get_module_logger("TableWorkflow", level="INFO") + + +class TableWorkflow: + def __init__(self, learnware_market): + self.learnware_market = learnware_market + + self.root_path = os.path.abspath(os.path.join(__file__, "..")) + self.learnware_pool_path = os.path.join(self.root_path, "data/learnware_pool") + self.learnware_zip_pool_path = os.path.join(self.root_path, "data/zips") + self.example_learnware_path = os.path.join(self.root_path, "data/example_files") + self.model_save_path = os.path.join(self.root_path, "data/uploader_models") + self.result_path = os.path.join(self.root_path, "results") + + os.makedirs(self.learnware_pool_path, exist_ok=True) + os.makedirs(self.learnware_zip_pool_path, exist_ok=True) + os.makedirs(self.model_save_path, exist_ok=True) + os.makedirs(self.result_path, exist_ok=True) + + def _init_dataset(self): + self._prepare_data() + self._prepare_model() + + @staticmethod + def _limited_data(method, test_info, loss_func): + all_scores = [] + for subset in test_info["train_subsets"]: + subset_scores = [] + for sample in subset: + x_train, y_train = sample["x_train"], sample["y_train"] + model = method(x_train, y_train, test_info) + subset_scores.append(loss_func(model.predict(test_info["test_x"]), test_info["test_y"])) + all_scores.append(np.mean(subset_scores)) + return all_scores + + # @staticmethod + # def _limited_data_single_learnware(method, test_info, learnware): + # test_info['single_learnware'] = learnware + # return TableWorkflow._limited_data(method, test_info) + + def test_method(self, test_info, recorders, loss_func=loss_func_rmse): + method_name_full = test_info["method_name"] + method_name = method_name_full if method_name_full == "user_model" else "_".join(method_name_full.split("_")[1:]) + user, idx = test_info["user"], test_info["idx"] + recorder = recorders[method_name_full] + + save_root_path = os.path.join(self.curves_result_path, f"{user}/{user}_{idx}") + os.makedirs(save_root_path, exist_ok=True) + save_path = os.path.join(save_root_path, f"{method_name}.json") + + if method_name == "single_aug": + if test_info["force"] or recorder.should_test_method(user, idx, save_path): + # with Pool() as pool: + # learnware_results = pool.starmap( + # self._limited_data_single_learnware, + # [(test_methods[method_name], test_info, learnware) for learnware in test_info['learnwares']] + # ) + # for scores in learnware_results: + # recorders[method_name].record(user, idx, scores) + + for learnware in test_info['learnwares']: + test_info['single_learnware'] = learnware + scores = self._limited_data(test_methods[method_name_full], test_info, loss_func) + recorder.record(user, idx, scores) + + process_single_aug(user, idx, scores, recorders, save_root_path) + recorder.save(save_path) + logger.info(f"Method {method_name} on {user}_{idx} finished") + else: + process_single_aug(user, idx, recorder.data[user][str(idx)], recorders, save_root_path) + logger.info(f"Method {method_name} on {user}_{idx} already exists") + else: + if test_info["force"] or recorder.should_test_method(user, idx, save_path): + scores = self._limited_data(test_methods[method_name_full], test_info, loss_func) + recorder.record(user, idx, scores) + recorder.save(save_path) + logger.info(f"Method {method_name} on {user}_{idx} finished") + else: + logger.info(f"Method {method_name} on {user}_{idx} already exists") + + def prepare_market(self, name, market_id, regenerate_flag=False): + if regenerate_flag: + self._init_dataset() + market = instantiate_learnware_market(name=name, market_id=market_id, rebuild=True) + client = LearnwareClient() + + full_descriptions_dir = os.path.join("./data/full_descriptions.json") + with open(full_descriptions_dir, "rb") as f: + full_descriptions = json.load(f) + + for uploader in self.learnware_market: + data_loader = DataLoader(uploader) + idx_list = data_loader.get_shop_ids() + for i, idx in enumerate(idx_list): + feature_descriptions = data_loader.get_raw_data(idx)[-1] + feature_dim = len(feature_descriptions) + feature_descriptions_dict = {str(i): feature_descriptions[i] for i in range(feature_dim)} + input_description = {"Dimension": feature_dim, "Description": feature_descriptions_dict} + + name_and_description = full_descriptions[uploader][i] + semantic_spec = client.create_semantic_specification( + name=name_and_description["name"], + description=name_and_description["description"], + data_type="Table", + task_type="Regression", + library_type="Others", + license=["MIT"], + scenarios=["Business"], + input_description=input_description, + output_description=output_description, + ) + + learnware_zip_path = self._prepare_learnware(data_loader, idx) + market.add_learnware(learnware_zip_path, semantic_spec) + + # if use pretrained market mapping + if name == "hetero": + learnware_ids = market.get_learnware_ids() + market.learnware_organizer._update_learware_hetero_spec(learnware_ids) + + logger.info("Total Item: %d" % (len(market))) + + def _prepare_data(self): + for uploader in self.learnware_market: + data_loader = DataLoader(uploader) + data_loader.regenerate_raw_data() + + def _prepare_model(self, use_exist=True): + self.learnware_num = 0 + for uploader in self.learnware_market: + data_loader = DataLoader(uploader) + idx_list = data_loader.get_shop_ids() + self.learnware_num += len(idx_list) + for idx in idx_list: + logger.info(f"Train on uploader: {uploader}_{idx}") + idx_model_save_path = os.path.join(self.model_save_path, f"{uploader}_{idx}.out") + if not use_exist: + x_train, y_train, x_val, y_val, _ = data_loader.get_raw_data(idx) + data_loader.train_a_model(x_train, y_train, x_val, y_val, save_dir=idx_model_save_path) + else: + uploader_dataset = uploader.split("_")[0] + model = data_loader.get_model(idx) + if uploader_dataset == "corporacion": + model.save_model(idx_model_save_path) + elif uploader_dataset == "pfs": + joblib.dump(model, idx_model_save_path) + else: + logger.error(f"Not supported dataset type {uploader_dataset}") + + logger.info(f"Model saved to {idx_model_save_path}") + + def _prepare_learnware(self, data_loader, idx): + zip_path = os.path.join(self.learnware_zip_pool_path, f"{data_loader.dataset}_{idx}") + dir_path = os.path.join(self.learnware_pool_path, f"{data_loader.dataset}_{idx}") + model_path = os.path.join(self.model_save_path, f"{data_loader.dataset}_{idx}.out") + os.makedirs(dir_path, exist_ok=True) + + stat_spec, _ = data_loader.get_rkme(idx) + init_file = os.path.join(dir_path, "__init__.py") + yaml_file = os.path.join(dir_path, "learnware.yaml") + env_file = os.path.join(dir_path, "environment.yaml") + model_file = os.path.join(dir_path, "model.out") + + stat_spec.save(os.path.join(dir_path, "rkme.json")) + copyfile(os.path.join(self.example_learnware_path, f"{data_loader.dataset}/__init__.py"), init_file) + copyfile(os.path.join(self.example_learnware_path, f"{data_loader.dataset}/learnware.yaml"), yaml_file) + copyfile(os.path.join(self.example_learnware_path, "environment.yaml"), env_file) + copyfile(model_path, model_file) + + zip_file = zip_path + ".zip" + with zipfile.ZipFile(zip_file, "w") as zip_obj: + for foldername, _, filenames in os.walk(dir_path): + for filename in filenames: + file_path = os.path.join(foldername, filename) + zip_info = zipfile.ZipInfo(filename) + zip_info.compress_type = zipfile.ZIP_STORED + with open(file_path, "rb") as file: + zip_obj.writestr(zip_info, file.read()) + + rmtree(dir_path) # rm -r dir_path + return zip_file + + \ No newline at end of file diff --git a/examples/dataset_table_workflow/config.py b/examples/dataset_table_workflow/config.py new file mode 100644 index 0000000..c1e0ab8 --- /dev/null +++ b/examples/dataset_table_workflow/config.py @@ -0,0 +1,65 @@ +n_labeled_list = [100, 200, 500, 1000, 2000, 4000, 6000, 8000, 10000] +n_repeat_list = [10, 10, 10, 3, 3, 3, 3, 3, 3] + +styles = { + 'user_model': {"color": "navy", "marker": "o", "linestyle": "-"}, + 'select_score': {'color': 'gold', 'marker': 's', 'linestyle': '--'}, + 'oracle_score': {'color': 'darkorange', 'marker': '^', 'linestyle': '-.'}, + 'mean_score': {'color': 'gray', 'marker': 'x', 'linestyle': ':'}, + 'single_aug': {'color': 'gold', 'marker': 's', 'linestyle': '--'}, + 'multiple_avg': {'color': 'blue', 'marker': '*', 'linestyle': '-'}, + 'multiple_aug': {'color': 'purple', 'marker': 'd', 'linestyle': '--'}, + 'ensemble_pruning': {"color": "magenta", "marker": "d", "linestyle": "-."} +} + +labels = { + 'user_model': "User Model", + 'single_aug': "Single Learnware Reuse (Select)", + "select_score": "Single Learnware Reuse (Select)", + # "Single Learnware Reuse (Avg)", + # "Single Learnware Reuse (Oracle)", + 'multiple_aug': "Multiple Learnware Reuse (FeatAug)", + 'ensemble_pruning': "Multiple Learnware Reuse (EnsemblePrune)", + 'multiple_avg': "Multiple Learnware Reuse (Averaging)" +} + +output_description = { + "Dimension": 1, + "Description": { + "0": "Product sales on the date.", + }, +} + +user_semantic = { + "Data": {"Values": ["Table"], "Type": "Class"}, + "Task": {"Values": ["Regression"], "Type": "Class"}, + "Library": {"Values": ["Others"], "Type": "Class"}, + "Scenario": {"Values": ["Business"], "Type": "Tag"}, + "Description": {"Values": "", "Type": "String"}, + "Name": {"Values": "", "Type": "String"}, + "Output": output_description, +} + +align_model_params = { + "network_type": "ArbitraryMapping", # ["ArbitraryMapping", "BaseMapping", "BaseMapping_BN", "BaseMapping_Dropout"] + "num_epoch": 50, + "lr": 1e-5, + "dropout_ratio": 0.2, + "activation": "relu", + "use_bn": True, + "hidden_dims": [128, 256, 128, 256], +} + +market_mapping_params = { + "lr": 1e-4, # [5e-5, 1e-4, 2e-4, 5e-4], + "num_epoch": 50, + "batch_size": 64, # [64, 128, 256, 512, 1024], + "num_partition": 2, # [2, 3, 4], # num of column partitions for pos/neg sampling + "overlap_ratio": 0.7, # [0.1, 0.3, 0.5, 0.7], # specify the overlap ratio of column partitions during the CL + "hidden_dim": 256, # [64, 128, 256, 512, 768, 1024], # the dimension of hidden embeddings + "num_layer": 6, # [4, 6, 8, 10, 12, 14, 16, 20], # the number of transformer layers used in the encoder + "num_attention_head": 8, # [4, 8, 16], # the numebr of heads of multihead self-attention layer in the transformers, should be divisible by hidden_dim + "hidden_dropout_prob": 0.5, # [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6], # the dropout ratio in the transformer encoder + "ffn_dim": 512, # [128, 256, 512, 768, 1024], # the dimension of feed-forward layer in the transformer layer + "activation": "leakyrelu", +} diff --git a/examples/dataset_table_workflow/hetero.py b/examples/dataset_table_workflow/hetero.py new file mode 100644 index 0000000..267a2b1 --- /dev/null +++ b/examples/dataset_table_workflow/hetero.py @@ -0,0 +1,225 @@ +import os +import warnings +from collections import defaultdict +from functools import partial +warnings.filterwarnings("ignore") + +import json +import numpy as np +from matplotlib import pyplot as plt +import learnware.specification as specification +from learnware.logger import get_module_logger +from learnware.market import instantiate_learnware_market, BaseUserInfo +from learnware.reuse import AveragingReuser, FeatureAlignLearnware +from multiprocessing import Pool + +from benchmarks import DataLoader +from examples.dataset_table_workflow.methods import * +from base import TableWorkflow, user_semantic +from config import align_model_params +from utils import Recorder, plot_performance_curves, analyze_performance + +logger = get_module_logger("hetero_test", level="INFO") +learnware_market = ["pfs_default", "pfs_denis", "corporacion_bojan", "corporacion_lee", "corporacion_lingzhi"] +default_users = ["m5_default"] # "m5_default", "m5_kkiller", "m5_rana" +n_labeled_list = [100, 200, 500, 1000, 2000] +n_repeat_list = [10, 10, 10, 3, 3] + +class HeterogeneousWorkflow(TableWorkflow): + def __init__(self, reload_market=False, regenerate_flag=False): + super(HeterogeneousWorkflow, self).__init__(learnware_market) + self.curves_result_path = os.path.join(self.result_path, 'curves') + self.unlabeled_res_path = os.path.join(self.result_path, 'unlabeled') + self.figs_result_path = os.path.join(self.result_path, "figs") + + os.makedirs(self.curves_result_path, exist_ok=True) + os.makedirs(self.unlabeled_res_path, exist_ok=True) + os.makedirs(self.figs_result_path, exist_ok=True) + + if reload_market: + self.prepare_market(name="hetero", market_id="heterogeneous", regenerate_flag=regenerate_flag) + + def test_hetero_unlabeled(self): + hetero_market = instantiate_learnware_market(market_id="heterogeneous", name="hetero") + logger.info("Total Item: %d" % len(hetero_market)) + + select_list = defaultdict(list) + avg_list = defaultdict(list) + oracle_list = defaultdict(list) + improve_list = defaultdict(list) + ensemble_score_list = defaultdict(list) + user_model_score_lists = defaultdict(list) + + user_model_method = partial(self._limited_data, HeteroMethods.user_model_score) + + for user in default_users: + user_unlabeld_res_path = os.path.join(self.unlabeled_res_path, f"{user}.json") + if os.path.exists(user_unlabeld_res_path): + with open(user_unlabeld_res_path, "rb") as file: + unlabeld_res = json.load(file) + select_list[user] = unlabeld_res["select_list"] + avg_list[user] = unlabeld_res["avg_list"] + oracle_list[user] = unlabeld_res["oracle_list"] + ensemble_score_list[user] = unlabeld_res["ensemble_score_list"] + user_model_score_lists[user] = unlabeld_res["user_model_score_lists"] + else: + data_loader = DataLoader(user) + idx_list = data_loader.get_shop_ids() + for idx in idx_list: + _, _, test_x, test_y, feature_descriptions = data_loader.get_raw_data(idx) + train_x_list, _ = data_loader.get_labeled_training_data(idx, size_list=n_labeled_list, n_repeat_list=n_repeat_list) + user_stat_spec = specification.RKMETableSpecification() + user_stat_spec.generate_stat_spec_from_data(X=test_x) + + feature_dim = len(feature_descriptions) + feature_descriptions_dict = {str(i): feature_descriptions[i] for i in range(feature_dim)} + input_description = { + "Dimension": feature_dim, + "Description": feature_descriptions_dict + } + user_semantic["Input"] = input_description + user_info = BaseUserInfo(semantic_spec=user_semantic, stat_info={"RKMETableSpecification": user_stat_spec}) + logger.info(f"Searching Market for user: {user}_{idx}") + + search_result = hetero_market.search_learnware(user_info) + single_result = search_result.get_single_results() + multiple_result = search_result.get_multiple_results() + + logger.info(f"hetero search result of user {user}_{idx}:") + logger.info( + f"single model num: {len(single_result)}, max_score: {single_result[0].score}, min_score: {single_result[-1].score}" + ) + + l = len(single_result) + rmse_list = [] + for idx in range(l): + hetero_learnware = FeatureAlignLearnware(single_result[idx].learnware, **align_model_params) + hetero_learnware.align(user_rkme=user_stat_spec) + pred_y = hetero_learnware.predict(test_x) + rmse_list.append(loss_func_rmse(pred_y, test_y)) + + logger.info( + f"Top1-score: {single_result[0].score}, learnware_id: {single_result[0].learnware.id}, rmse: {rmse_list[0]}" + ) + + if len(multiple_result) > 0: + mixture_id = " ".join([learnware.id for learnware in multiple_result[0].learnwares]) + logger.info(f"mixture_score: {multiple_result[0].score}, mixture_learnware: {mixture_id}") + mixture_learnware_list = [] + for learnware in multiple_result[0].learnwares: + hetero_learnware = FeatureAlignLearnware(learnware, **align_model_params) + hetero_learnware.align(user_rkme=user_stat_spec) + mixture_learnware_list.append(hetero_learnware) + else: + hetero_learnware = FeatureAlignLearnware(single_result[0].learnware, **align_model_params) + hetero_learnware.align(user_rkme=user_stat_spec) + mixture_learnware_list = [hetero_learnware] + + # test user model + user_model_score_list = user_model_method(train_x_list, test_x, test_y, data_loader) + + # test reuse (ensemble) + reuse_ensemble = AveragingReuser(learnware_list=mixture_learnware_list, mode="mean") + ensemble_predict_y = reuse_ensemble.predict(user_data=test_x) + ensemble_score = loss_func_rmse(ensemble_predict_y, test_y) + ensemble_score_list[user].append(ensemble_score) + logger.info(f"mixture reuse rmse (ensemble): {ensemble_score}") + + select_list[user].append(rmse_list[0]) + avg_list[user].append(np.mean(rmse_list)) + oracle_list[user].append(np.min(rmse_list)) + improve_list[user].append((np.mean(rmse_list) - rmse_list[0]) / np.mean(rmse_list)) + user_model_score_lists[user].append(user_model_score_list) + + logger.info(f"Saving unlabeled results for User: {user}") + res = { + "select_list": select_list[user], + "avg_list": avg_list[user], + "oracle_list": oracle_list[user], + "ensemble_score_list": ensemble_score_list[user], + "user_model_score_lists": user_model_score_lists[user] + } + with open(user_unlabeld_res_path, "w") as file: + json.dump(res, file, indent=4) + + for user in default_users: + logger.info(f"User Dataset: {user}") + logger.info( + "RMSE of selected learnware: %.3f +/- %.3f, Average performance: %.3f +/- %.3f, Oracle performace: %.3f +/- %.3f" + % (np.mean(select_list[user]), np.std(select_list[user]), np.mean(avg_list[user]), np.std(avg_list[user]), np.mean(oracle_list[user]), np.std(oracle_list[user])) + ) + logger.info( + "Averaging Ensemble Reuse Performance: %.3f +/- %.3f" + % (np.mean(ensemble_score_list[user]), np.std(ensemble_score_list[user])) + ) + for idx, n_labeled in enumerate(n_labeled_list): + n_labeled_score_list = [lst[idx] for lst in user_model_score_lists[user]] + logger.info( + "User Model with %d data: %.3f +/ %.3f" + % (n_labeled, np.mean(n_labeled_score_list), np.std(n_labeled_score_list)) + ) + + def test_hetero_labeled(self): + hetero_market = instantiate_learnware_market(market_id="heterogeneous", name="hetero") + logger.info("Total Items: %d" % len(hetero_market)) + + methods = ["user_model", "hetero_single_aug", "hetero_multiple_aug", "hetero_multiple_avg", "hetero_ensemble_pruning"] + methods_to_test = ["hetero_multiple_avg", "hetero_ensemble_pruning"] + recorders = {method: Recorder() for method in methods + ["select_score", "oracle_score", "mean_score"]} + + for user in default_users: + data_loader = DataLoader(user) + idx_list = data_loader.get_shop_ids() + for idx in idx_list: + _, _, test_x, test_y, feature_descriptions = data_loader.get_raw_data(idx) + train_subsets = data_loader.get_labeled_training_data( + idx, + size_list=n_labeled_list, + n_repeat_list=n_repeat_list + ) + user_stat_spec = specification.RKMETableSpecification() + user_stat_spec.generate_stat_spec(X=test_x) + + input_description = { + "Dimension": len(feature_descriptions), + "Description": {str(i): feature_descriptions[i] for i in range(len(feature_descriptions))} + } + user_semantic["Input"] = input_description + user_info = BaseUserInfo(semantic_spec=user_semantic, stat_info={"RKMETableSpecification": user_stat_spec}) + logger.info(f"Searching Market for user: {user}_{idx}") + + search_result = hetero_market.search_learnware(user_info, max_search_num=10) + single_result = search_result.get_single_results() + multiple_result = search_result.get_multiple_results() + + if len(multiple_result) > 0: + mixture_id = " ".join([learnware.id for learnware in multiple_result[0].learnwares]) + logger.info(f"Mixture score: {multiple_result[0].score}, Mixture learnware: {mixture_id}") + mixture_learnware_list = multiple_result[0].learnwares + else: + mixture_learnware_list = [single_result[0].learnware] + + logger.info(f"Hetero search result of user {user}_{idx}: mixture learnware num: {len(mixture_learnware_list)}") + + test_info = {"user": user, "idx": idx, "train_subsets": train_subsets, "test_x": test_x, "test_y": test_y} + common_config = {"user_rkme": user_stat_spec, "multiple_learnwares": mixture_learnware_list} + method_configs = { + "user_model": {"data_loader": data_loader}, + "hetero_single_aug": {"user_rkme": user_stat_spec, "learnwares": [single_result[0].learnware]}, + "hetero_multiple_aug": common_config, + "hetero_multiple_avg": common_config, + "hetero_ensemble_pruning": common_config + } + + for method_name in methods: + test_info["method_name"] = method_name + test_info["force"] = method_name in methods_to_test + test_info.update(method_configs[method_name]) + self.test_method(test_info, recorders, loss_func=loss_func_rmse) + + for method, recorder in recorders.items(): + recorder.save(os.path.join(self.curves_result_path, f"{user}/{user}_{method}_performance.json")) + + methods_to_plot = ["user_model", "select_score", "hetero_multiple_avg", "hetero_ensemble_pruning"] + plot_performance_curves(user, {method: recorders[method] for method in methods_to_plot}, n_labeled_list=n_labeled_list) + \ No newline at end of file diff --git a/examples/dataset_table_workflow/homo.py b/examples/dataset_table_workflow/homo.py new file mode 100644 index 0000000..efa6deb --- /dev/null +++ b/examples/dataset_table_workflow/homo.py @@ -0,0 +1,204 @@ +import os +import warnings +from collections import defaultdict + +warnings.filterwarnings("ignore") + +import numpy as np +from matplotlib import pyplot as plt +from functools import partial +import learnware.specification as specification +from learnware.logger import get_module_logger +from learnware.market import instantiate_learnware_market, BaseUserInfo +from learnware.reuse import AveragingReuser, JobSelectorReuser, EnsemblePruningReuser + +from benchmarks import DataLoader +from base import TableWorkflow, user_semantic +from methods import * +from config import n_labeled_list, n_repeat_list +from utils import Recorder, plot_performance_curves + +logger = get_module_logger("corporacion_test", level="INFO") +learnware_market = ["corporacion_bojan", "corporacion_lee", "corporacion_lingzhi"] +users = ["corporacion_lingzhi"] + +class CorporacionDatasetWorkflow(TableWorkflow): + def __init__(self, reload_market=False, regenerate_flag=False): + super(CorporacionDatasetWorkflow, self).__init__(learnware_market) + self.curves_result_path = os.path.join(self.result_path, "curves") + self.figs_result_path = os.path.join(self.result_path, "figs") + + os.makedirs(self.curves_result_path, exist_ok=True) + os.makedirs(self.figs_result_path, exist_ok=True) + + if reload_market: + self.prepare_market(name="easy", market_id="corporacion", regenerate_flag=regenerate_flag) + + def test_homo_unlabeled(self): + corporacion_market = instantiate_learnware_market(market_id="corporacion") + logger.info("Total Item: %d" % len(corporacion_market)) + + learnware_rmse_list = defaultdict(list) + job_selector_score_list = defaultdict(list) + ensemble_score_list = defaultdict(list) + pruning_score_list = defaultdict(list) + + for user in learnware_market: + corporacion = DataLoader(user) + idx_list = corporacion.get_shop_ids() + for idx in idx_list: + _, _, test_x, test_y, _ = corporacion.get_raw_data(idx) + user_stat_spec = specification.RKMETableSpecification() + user_stat_spec.generate_stat_spec_from_data(X=test_x) + user_info = BaseUserInfo( + semantic_spec=user_semantic, stat_info={"RKMETableSpecification": user_stat_spec} + ) + logger.info(f"Searching Market for user: {user}_{idx}") + + search_result = corporacion_market.search_learnware(user_info, max_search_num=10) + single_result = search_result.get_single_results() + multiple_result = search_result.get_multiple_results() + + logger.info(f"search result of user {user}_{idx}:") + logger.info( + f"single model num: {len(single_result)}, max_score: {single_result[0].score}, min_score: {single_result[-1].score}" + ) + + l = len(single_result) + rmse_list = [] + for idx in range(l): + learnware = single_result[idx].learnware + pred_y = learnware.predict(test_x) + rmse_list.append(loss_func_mse(pred_y, test_y)) + logger.info( + f"Top1-score: {single_result[0].score}, learnware_id: {single_result[0].learnware.id}, rmse: {rmse_list[0]}" + ) + + if len(multiple_result) > 0: + mixture_id = " ".join([learnware.id for learnware in multiple_result[0].learnwares]) + logger.info(f"mixture_score: {multiple_result[0].score}, mixture_learnware: {mixture_id}") + mixture_learnware_list = multiple_result[0].learnwares + else: + mixture_learnware_list = [single_result[0].learnware] + + # test reuse (job selector) + reuse_baseline = JobSelectorReuser(learnware_list=mixture_learnware_list, herding_num=100) + reuse_predict = reuse_baseline.predict(user_data=test_x) + reuse_score = loss_func_mse(reuse_predict, test_y) + job_selector_score_list[user].append(reuse_score) + logger.info(f"mixture reuse rmse (job selector): {reuse_score}") + + # test reuse (ensemble) + reuse_ensemble = AveragingReuser(learnware_list=mixture_learnware_list, mode="mean") + ensemble_predict_y = reuse_ensemble.predict(user_data=test_x) + ensemble_score = loss_func_mse(ensemble_predict_y, test_y) + ensemble_score_list[user].append(ensemble_score) + logger.info(f"mixture reuse rmse (ensemble): {ensemble_score}") + + # test reuse (ensemblePruning) + reuse_pruning = EnsemblePruningReuser(learnware_list=mixture_learnware_list, mode="regression") + pruning_predict_y = reuse_pruning.predict(user_data=test_x) + pruning_score = loss_func_mse(pruning_predict_y, test_y) + pruning_score_list[user].append(pruning_score) + logger.info(f"mixture reuse rmse (ensemble Pruning): {pruning_score}\n") + + learnware_rmse_list[user].append(rmse_list) + + for user in learnware_market: + logger.info(f"User Dataset: {user}") + + single_list = np.array(learnware_rmse_list[user]) + select_score_list = [lst[0] for lst in single_list] + avg_score_list = [np.mean(lst, axis=0) for lst in single_list] + oracle_score_list = [np.min(lst, axis=0) for lst in single_list] + + logger.info( + "RMSE of selected learnware: %.3f +/- %.3f, Average performance: %.3f +/- %.3f, Oracle performace: %.3f +/- %.3f" + % ( + np.mean(select_score_list), + np.std(select_score_list), + np.mean(avg_score_list), + np.std(avg_score_list), + np.mean(oracle_score_list), + np.std(oracle_score_list), + ) + ) + logger.info( + "Average Job Selector Reuse Performance: %.3f +/- %.3f" + % (np.mean(job_selector_score_list[user]), np.std(job_selector_score_list[user])) + ) + logger.info( + "Averaging Ensemble Reuse Performance: %.3f +/- %.3f" + % (np.mean(ensemble_score_list[user]), np.std(ensemble_score_list[user])) + ) + logger.info( + "Selective Ensemble Reuse Performance: %.3f +/- %.3f" + % (np.mean(pruning_score_list[user]), np.std(pruning_score_list[user])) + ) + + def test_homo_labeled(self): + corporacion_market = instantiate_learnware_market(market_id="corporacion") + logger.info("Total Item: %d" % len(corporacion_market)) + + methods = ["user_model", "homo_single_aug", "homo_multiple_aug", "homo_multiple_avg", "homo_ensemble_pruning"] + recorders = {method: Recorder() for method in methods} + + methods_to_retest = [] + + for user in users: + data_loader = DataLoader(user) + idx_list = data_loader.get_shop_ids() + for idx in idx_list: + _, _, test_x, test_y, _ = data_loader.get_raw_data(idx) + train_subsets = data_loader.get_labeled_training_data( + idx, + size_list=n_labeled_list, + n_repeat_list=n_repeat_list + ) + + user_stat_spec = specification.RKMETableSpecification() + user_stat_spec.generate_stat_spec_from_data(X=test_x) + user_info = BaseUserInfo( + semantic_spec=user_semantic, stat_info={"RKMETableSpecification": user_stat_spec} + ) + logger.info(f"Searching Market for user: {user}_{idx}") + + search_result = corporacion_market.search_learnware(user_info, max_search_num=10) + single_result = search_result.get_single_results() + multiple_result = search_result.get_multiple_results() + + logger.info(f"search result of user {user}_{idx}:") + logger.info( + f"single model num: {len(single_result)}, max_score: {single_result[0].score}, min_score: {single_result[-1].score}" + ) + + if len(multiple_result) > 0: + mixture_id = " ".join([learnware.id for learnware in multiple_result[0].learnwares]) + logger.info(f"mixture_score: {multiple_result[0].score}, mixture_learnware: {mixture_id}") + mixture_learnware_list = multiple_result[0].learnwares + else: + mixture_learnware_list = [single_result[0].learnware] + + test_info = {"user": user, "idx": idx, "train_subsets": train_subsets, "test_x": test_x, "test_y": test_y} + common_config = {"multiple_learnwares": mixture_learnware_list} + method_configs = { + "user_model": {"data_loader": data_loader}, + "homo_single_aug": {"single_learnware": [single_result[0].learnware]}, + "homo_multiple_aug": common_config, + "homo_multiple_avg": common_config, + "homo_ensemble_pruning": common_config + } + + for method_name in methods: + # self.test_method(method_name, HeteroScoringMethods.__dict__[f"{method_name}_score"], recorders, test_info) + logger.info(f"Testing method {method_name}") + test_info["method_name"] = method_name + test_info["force"] = method_name in methods_to_retest + test_info.update(method_configs[method_name]) + self.test_method(test_info, recorders, loss_func=loss_func_mse) + + for method, recorder in recorders.items(): + recorder.save(os.path.join(self.curves_result_path, f"{user}_{method}_performance.json")) + + methods_to_plot = ["user_model", "homo_single_aug", "homo_ensemble_pruning"] + plot_performance_curves(user, {method: recorders[method] for method in methods_to_plot}, task="Homo", n_labeled_list=n_labeled_list) \ No newline at end of file diff --git a/examples/dataset_table_workflow/main.py b/examples/dataset_table_workflow/main.py new file mode 100644 index 0000000..19911b8 --- /dev/null +++ b/examples/dataset_table_workflow/main.py @@ -0,0 +1,34 @@ +import fire +from pyinstrument import Profiler + +from dataset_corporacion_workflow import CorporacionDatasetWorkflow +from dataset_heterogeneous_workflow import HeterogeneousWorkflow + +workflow_mapping = { + "test_homo_unlabeled": CorporacionDatasetWorkflow, + "test_homo_labeled": CorporacionDatasetWorkflow, + "test_hetero_unlabeled": HeterogeneousWorkflow, + "test_hetero_labeled": HeterogeneousWorkflow, +} + + +def main(): + def dispatch(command): + if command in workflow_mapping: + workflow_class = workflow_mapping[command] + workflow_instance = workflow_class() + getattr(workflow_instance, command)() + else: + print(f"No workflow found for command: {command}") + + fire.Fire(dispatch) + + +if __name__ == "__main__": + profiler = Profiler() + profiler.start() + + main() + + profiler.stop() + profiler.print() diff --git a/examples/dataset_table_workflow/methods.py b/examples/dataset_table_workflow/methods.py new file mode 100644 index 0000000..11580fb --- /dev/null +++ b/examples/dataset_table_workflow/methods.py @@ -0,0 +1,105 @@ +import numpy as np +from sklearn.metrics import mean_squared_error +from sklearn.model_selection import train_test_split # Add missing import +from loguru import logger + +from learnware.reuse import AveragingReuser, EnsemblePruningReuser, FeatureAugmentReuser, HeteroMapAlignLearnware +from examples.dataset_table_workflow.config import align_model_params + +def loss_func_rmse(y_true, y_pred): + return np.sqrt(mean_squared_error(y_true, y_pred)) + +def loss_func_mse(y_true, y_pred): + return mean_squared_error(y_true, y_pred) + +def user_model_score(x_train, y_train, test_info): + data_loader = test_info["data_loader"] + x_train, x_val, y_train, y_val = train_test_split(x_train, y_train, test_size=0.2, random_state=42) + user_model = data_loader.train_a_model(x_train, y_train, x_val, y_val) + return user_model + +class HomoScoringMethods: + @staticmethod + def single_aug_score(x_train, y_train, test_info): + single_learnware = test_info["single_learnware"] + reuse_single_augment = FeatureAugmentReuser(single_learnware, mode="regression") + reuse_single_augment.fit(x_train=x_train, y_train=y_train) + return reuse_single_augment + + @staticmethod + def multiple_aug_score(x_train, y_train, test_info): + multiple_learnwares = test_info["multiple_learnwares"] + reuse_multiple_augment = FeatureAugmentReuser(multiple_learnwares, mode="regression") + reuse_multiple_augment.fit(x_train=x_train, y_train=y_train) + return reuse_multiple_augment + + @staticmethod + def multiple_avg_score(x_train, y_train, test_info): + multiple_learnwares = test_info["multiple_learnwares"] + reuse_multiple_avg = AveragingReuser(multiple_learnwares, mode="mean") + return reuse_multiple_avg + + @staticmethod + def multiple_ensemble_pruning_score(x_train, y_train, test_info): + multiple_learnwares = test_info["multiple_learnwares"] + if len(multiple_learnwares) == 1: + return multiple_learnwares[0] + reuse_pruning = EnsemblePruningReuser(multiple_learnwares, mode="regression") + reuse_pruning.fit(val_X=x_train, val_y=y_train) + return reuse_pruning + + +class HeteroMethods: + @staticmethod + def create_hetero_learnware_list(learnware_list, user_rkme, x_train, y_train): # Fix typo in method name + hetero_learnware_list = [] + for learnware in learnware_list: + hetero_learnware = HeteroMapAlignLearnware(learnware, mode="regression", **align_model_params) + hetero_learnware.align(user_rkme, x_train, y_train) + hetero_learnware_list.append(hetero_learnware) + return hetero_learnware_list + + @staticmethod + def single_aug_score(x_train, y_train, test_info): + user_rkme, single_learnware = test_info["user_rkme"], test_info["single_learnware"] + reuse_single_augment = HeteroMapAlignLearnware(single_learnware, mode="regression", **align_model_params) + reuse_single_augment.align(user_rkme=user_rkme, x_train=x_train, y_train=y_train) + return reuse_single_augment + + @staticmethod + def multiple_aug_score(x_train, y_train, test_info): + user_rkme, multiple_learnwares = test_info["user_rkme"], test_info["multiple_learnwares"] + hetero_learnware_list = HeteroMethods.create_hetero_learnware_list(multiple_learnwares, user_rkme, x_train, y_train) + reuse_multiple_augment = FeatureAugmentReuser(hetero_learnware_list, mode="regression") + reuse_multiple_augment.fit(x_train=x_train, y_train=y_train) + return reuse_multiple_augment + + @staticmethod + def multiple_ensemble_pruning_score(x_train, y_train, test_info): + user_rkme, multiple_learnwares = test_info["user_rkme"], test_info["multiple_learnwares"] + hetero_learnware_list = HeteroMethods.create_hetero_learnware_list(multiple_learnwares, user_rkme, x_train, y_train) + if len(hetero_learnware_list) == 1: + return hetero_learnware_list[0] + reuse_pruning = EnsemblePruningReuser(hetero_learnware_list, mode="regression") + reuse_pruning.fit(val_X=x_train, val_y=y_train) + return reuse_pruning + + @staticmethod + def multiple_avg_score(x_train, y_train, test_info): + user_rkme, multiple_learnwares = test_info["user_rkme"], test_info["multiple_learnwares"] + hetero_learnware_list = HeteroMethods.create_hetero_learnware_list(multiple_learnwares, user_rkme, x_train, y_train) + reuse_multiple_avg = AveragingReuser(hetero_learnware_list, mode="mean") + return reuse_multiple_avg + + +test_methods = { + "user_model": user_model_score, + "hetero_single_aug": HeteroMethods.single_aug_score, + "hetero_multiple_aug": HeteroMethods.multiple_aug_score, + "hetero_multiple_avg": HeteroMethods.multiple_avg_score, + "hetero_ensemble_pruning": HeteroMethods.multiple_ensemble_pruning_score, + "homo_single_aug": HomoScoringMethods.single_aug_score, + "homo_multiple_aug": HomoScoringMethods.multiple_aug_score, + "homo_multiple_avg": HomoScoringMethods.multiple_avg_score, + "homo_ensemble_pruning": HomoScoringMethods.multiple_ensemble_pruning_score +} \ No newline at end of file diff --git a/examples/dataset_table_workflow/utils.py b/examples/dataset_table_workflow/utils.py new file mode 100644 index 0000000..d37e4bd --- /dev/null +++ b/examples/dataset_table_workflow/utils.py @@ -0,0 +1,109 @@ +import os +from collections import defaultdict + +import json +import matplotlib.pyplot as plt +import numpy as np +from loguru import logger +import traceback + +from examples.dataset_table_workflow.config import * +from benchmarks.config import default_size_list + +class Recorder: + def __init__(self, headers=["Mean", "Std Dev"], formats=["{:.2f}", "{:.2f}"]): + assert len(headers) == len(formats), "Headers and formats length must match." + self.data = defaultdict(lambda: defaultdict(list)) + self.headers = headers + self.formats = formats + + def record(self, user, idx, scores): + self.data[user][idx].append(scores) + + def get_performance_data(self, user): + if user in self.data: + return [idx_scores for idx_scores in self.data[user].values()] + else: + return [] + + def save(self, path): + with open(path, "w") as f: + json.dump(self.data, f, indent=4, default=list) + + def load(self, path): + with open(path, "r") as f: + self.data = json.load(f, object_hook=lambda x: defaultdict(list, x)) + + def should_test_method(self, user, idx, path): + if os.path.exists(path): + self.load(path) + return user not in self.data or str(idx) not in self.data[user] + return True + + +def process_single_aug(user, idx, scores, recorders, root_path): + try: + scores_array = np.array(scores) + while scores_array.ndim < 3: + scores_array = scores_array[np.newaxis, :] + select_scores = scores_array[:, 0, :].tolist() + mean_scores = np.mean(scores_array, axis=1).tolist() + oracle_scores = np.min(scores_array, axis=1).tolist() + + for method_name, scores in zip(["select_score", "mean_score", "oracle_score"], + [select_scores, mean_scores, oracle_scores]): + recorders[method_name].record(user, idx, scores) + save_path = os.path.join(root_path, f"{method_name}_performance.json") + recorders[method_name].save(save_path) + except Exception as e: + error_message = traceback.format_exc() + logger.error(f"Error in process_single_aug for user {user}, idx {idx}: {error_message}") + + +def analyze_performance(user, recorders): + oracle_score_list = recorders["hetero_oracle_score"].get_performance_data(user) + select_score_list = recorders["hetero_select_score"].get_performance_data(user) + multi_avg_score_list = recorders["hetero_multiple_avg"].get_performance_data(user) + mean_differences = {} + + for user_id in range(len(oracle_score_list)): + select_scores = select_score_list[user_id] + oracle_scores = oracle_score_list[user_id] + mean_difference = np.mean(select_scores) - np.mean(oracle_scores) + mean_differences[user_id] = mean_difference + + sorted_user_ids = sorted(mean_differences, key=mean_differences.get, reverse=True) + + for user_id in sorted_user_ids: + single_multi_diff = np.mean(select_score_list[user_id]) - np.mean(multi_avg_score_list[user_id]) + logger.info(f"{user}, {user_id}, {mean_differences[user_id]}, {single_multi_diff}") + + +def plot_performance_curves(user, recorders, task="Hetero", n_labeled_list=default_size_list): + plt.figure(figsize=(10, 6)) + + for method, recorder in recorders.items(): + if method == "hetero_single_aug": + continue + + user_data = recorder.get_performance_data(user) + + if user_data: + scores_array = np.array([np.array(lst) for lst in user_data]) + mean_scores = np.squeeze(np.mean(scores_array, axis=0)) + std_scores = np.squeeze(np.std(scores_array, axis=0)) + + method_plot = '_'.join(method.split('_')[1:]) if method not in ['user_model', 'oracle_score', 'select_score', 'mean_score'] else method + style = styles.get(method_plot, {"color": "black", "linestyle": "-"}) + plt.plot(range(len(n_labeled_list)), mean_scores, label=labels.get(method_plot), **style) + + std_scale = 0.2 if task == "Hetero" else 0.5 + plt.fill_between(range(len(n_labeled_list)), mean_scores - std_scale * std_scores, mean_scores + std_scale * std_scores, color=style["color"], alpha=0.2) + + plt.xticks(range(len(n_labeled_list)), n_labeled_list) + plt.xlabel('Sample Size') + plt.ylabel('RMSE') + plt.title(f'Table {task} Limited Labeled Data') + plt.legend() + plt.tight_layout() + plt.savefig(os.path.join('./results/figs', f"{user}_labeled_{list(recorders.keys())}.png"), bbox_inches="tight", dpi=700) \ No newline at end of file From 5729af6b20d3b18cdbac86fb046e42b03e4cf16d Mon Sep 17 00:00:00 2001 From: Gene Date: Tue, 26 Dec 2023 23:40:02 +0800 Subject: [PATCH 03/32] [MNT] init homo table workflow --- examples/base.py | 199 ------------ examples/dataset_table_workflow/base.py | 105 ++++++ examples/dataset_table_workflow/config.py | 106 +++++-- examples/dataset_table_workflow/homo.py | 333 +++++++++----------- examples/dataset_table_workflow/main.py | 34 -- examples/dataset_table_workflow/methods.py | 27 +- examples/dataset_table_workflow/train.py | 48 +++ examples/dataset_table_workflow/utils.py | 23 +- examples/dataset_table_workflow/workflow.py | 35 ++ 9 files changed, 454 insertions(+), 456 deletions(-) delete mode 100644 examples/base.py create mode 100644 examples/dataset_table_workflow/base.py delete mode 100644 examples/dataset_table_workflow/main.py create mode 100644 examples/dataset_table_workflow/train.py create mode 100644 examples/dataset_table_workflow/workflow.py diff --git a/examples/base.py b/examples/base.py deleted file mode 100644 index 4dfd4fb..0000000 --- a/examples/base.py +++ /dev/null @@ -1,199 +0,0 @@ -import os -import joblib -import zipfile -from shutil import copyfile, rmtree - -import json -from learnware.client import LearnwareClient -from learnware.logger import get_module_logger -from learnware.market import instantiate_learnware_market -from multiprocessing import Pool - -from benchmarks import DataLoader -from config import * -from methods import * -from utils import process_single_aug - -logger = get_module_logger("TableWorkflow", level="INFO") - - -class TableWorkflow: - def __init__(self, learnware_market): - self.learnware_market = learnware_market - - self.root_path = os.path.abspath(os.path.join(__file__, "..")) - self.learnware_pool_path = os.path.join(self.root_path, "data/learnware_pool") - self.learnware_zip_pool_path = os.path.join(self.root_path, "data/zips") - self.example_learnware_path = os.path.join(self.root_path, "data/example_files") - self.model_save_path = os.path.join(self.root_path, "data/uploader_models") - self.result_path = os.path.join(self.root_path, "results") - - os.makedirs(self.learnware_pool_path, exist_ok=True) - os.makedirs(self.learnware_zip_pool_path, exist_ok=True) - os.makedirs(self.model_save_path, exist_ok=True) - os.makedirs(self.result_path, exist_ok=True) - - def _init_dataset(self): - self._prepare_data() - self._prepare_model() - - @staticmethod - def _limited_data(method, test_info, loss_func): - all_scores = [] - for subset in test_info["train_subsets"]: - subset_scores = [] - for sample in subset: - x_train, y_train = sample["x_train"], sample["y_train"] - model = method(x_train, y_train, test_info) - subset_scores.append(loss_func(model.predict(test_info["test_x"]), test_info["test_y"])) - all_scores.append(np.mean(subset_scores)) - return all_scores - - # @staticmethod - # def _limited_data_single_learnware(method, test_info, learnware): - # test_info['single_learnware'] = learnware - # return TableWorkflow._limited_data(method, test_info) - - def test_method(self, test_info, recorders, loss_func=loss_func_rmse): - method_name_full = test_info["method_name"] - method_name = method_name_full if method_name_full == "user_model" else "_".join(method_name_full.split("_")[1:]) - user, idx = test_info["user"], test_info["idx"] - recorder = recorders[method_name_full] - - save_root_path = os.path.join(self.curves_result_path, f"{user}/{user}_{idx}") - os.makedirs(save_root_path, exist_ok=True) - save_path = os.path.join(save_root_path, f"{method_name}.json") - - if method_name == "single_aug": - if test_info["force"] or recorder.should_test_method(user, idx, save_path): - # with Pool() as pool: - # learnware_results = pool.starmap( - # self._limited_data_single_learnware, - # [(test_methods[method_name], test_info, learnware) for learnware in test_info['learnwares']] - # ) - # for scores in learnware_results: - # recorders[method_name].record(user, idx, scores) - - for learnware in test_info['learnwares']: - test_info['single_learnware'] = learnware - scores = self._limited_data(test_methods[method_name_full], test_info, loss_func) - recorder.record(user, idx, scores) - - process_single_aug(user, idx, scores, recorders, save_root_path) - recorder.save(save_path) - logger.info(f"Method {method_name} on {user}_{idx} finished") - else: - process_single_aug(user, idx, recorder.data[user][str(idx)], recorders, save_root_path) - logger.info(f"Method {method_name} on {user}_{idx} already exists") - else: - if test_info["force"] or recorder.should_test_method(user, idx, save_path): - scores = self._limited_data(test_methods[method_name_full], test_info, loss_func) - recorder.record(user, idx, scores) - recorder.save(save_path) - logger.info(f"Method {method_name} on {user}_{idx} finished") - else: - logger.info(f"Method {method_name} on {user}_{idx} already exists") - - def prepare_market(self, name, market_id, regenerate_flag=False): - if regenerate_flag: - self._init_dataset() - market = instantiate_learnware_market(name=name, market_id=market_id, rebuild=True) - client = LearnwareClient() - - full_descriptions_dir = os.path.join("./data/full_descriptions.json") - with open(full_descriptions_dir, "rb") as f: - full_descriptions = json.load(f) - - for uploader in self.learnware_market: - data_loader = DataLoader(uploader) - idx_list = data_loader.get_shop_ids() - for i, idx in enumerate(idx_list): - feature_descriptions = data_loader.get_raw_data(idx)[-1] - feature_dim = len(feature_descriptions) - feature_descriptions_dict = {str(i): feature_descriptions[i] for i in range(feature_dim)} - input_description = {"Dimension": feature_dim, "Description": feature_descriptions_dict} - - name_and_description = full_descriptions[uploader][i] - semantic_spec = client.create_semantic_specification( - name=name_and_description["name"], - description=name_and_description["description"], - data_type="Table", - task_type="Regression", - library_type="Others", - license=["MIT"], - scenarios=["Business"], - input_description=input_description, - output_description=output_description, - ) - - learnware_zip_path = self._prepare_learnware(data_loader, idx) - market.add_learnware(learnware_zip_path, semantic_spec) - - # if use pretrained market mapping - if name == "hetero": - learnware_ids = market.get_learnware_ids() - market.learnware_organizer._update_learware_hetero_spec(learnware_ids) - - logger.info("Total Item: %d" % (len(market))) - - def _prepare_data(self): - for uploader in self.learnware_market: - data_loader = DataLoader(uploader) - data_loader.regenerate_raw_data() - - def _prepare_model(self, use_exist=True): - self.learnware_num = 0 - for uploader in self.learnware_market: - data_loader = DataLoader(uploader) - idx_list = data_loader.get_shop_ids() - self.learnware_num += len(idx_list) - for idx in idx_list: - logger.info(f"Train on uploader: {uploader}_{idx}") - idx_model_save_path = os.path.join(self.model_save_path, f"{uploader}_{idx}.out") - if not use_exist: - x_train, y_train, x_val, y_val, _ = data_loader.get_raw_data(idx) - data_loader.train_a_model(x_train, y_train, x_val, y_val, save_dir=idx_model_save_path) - else: - uploader_dataset = uploader.split("_")[0] - model = data_loader.get_model(idx) - if uploader_dataset == "corporacion": - model.save_model(idx_model_save_path) - elif uploader_dataset == "pfs": - joblib.dump(model, idx_model_save_path) - else: - logger.error(f"Not supported dataset type {uploader_dataset}") - - logger.info(f"Model saved to {idx_model_save_path}") - - def _prepare_learnware(self, data_loader, idx): - zip_path = os.path.join(self.learnware_zip_pool_path, f"{data_loader.dataset}_{idx}") - dir_path = os.path.join(self.learnware_pool_path, f"{data_loader.dataset}_{idx}") - model_path = os.path.join(self.model_save_path, f"{data_loader.dataset}_{idx}.out") - os.makedirs(dir_path, exist_ok=True) - - stat_spec, _ = data_loader.get_rkme(idx) - init_file = os.path.join(dir_path, "__init__.py") - yaml_file = os.path.join(dir_path, "learnware.yaml") - env_file = os.path.join(dir_path, "environment.yaml") - model_file = os.path.join(dir_path, "model.out") - - stat_spec.save(os.path.join(dir_path, "rkme.json")) - copyfile(os.path.join(self.example_learnware_path, f"{data_loader.dataset}/__init__.py"), init_file) - copyfile(os.path.join(self.example_learnware_path, f"{data_loader.dataset}/learnware.yaml"), yaml_file) - copyfile(os.path.join(self.example_learnware_path, "environment.yaml"), env_file) - copyfile(model_path, model_file) - - zip_file = zip_path + ".zip" - with zipfile.ZipFile(zip_file, "w") as zip_obj: - for foldername, _, filenames in os.walk(dir_path): - for filename in filenames: - file_path = os.path.join(foldername, filename) - zip_info = zipfile.ZipInfo(filename) - zip_info.compress_type = zipfile.ZIP_STORED - with open(file_path, "rb") as file: - zip_obj.writestr(zip_info, file.read()) - - rmtree(dir_path) # rm -r dir_path - return zip_file - - \ No newline at end of file diff --git a/examples/dataset_table_workflow/base.py b/examples/dataset_table_workflow/base.py new file mode 100644 index 0000000..fe31bd6 --- /dev/null +++ b/examples/dataset_table_workflow/base.py @@ -0,0 +1,105 @@ +import os +import time +import pandas +import random +import tempfile +import numpy as np +from learnware.client import LearnwareClient +from learnware.logger import get_module_logger +from learnware.market import instantiate_learnware_market +from learnware.tests.benchmarks import LearnwareBenchmark + +from config import * +from methods import * +from utils import process_single_aug + +logger = get_module_logger("base_table", level="INFO") + + +class TableWorkflow: + def __init__(self, benchmark_config, name="easy", rebuild=False): + self.root_path = os.path.abspath(os.path.join(__file__, "..")) + self.result_path = os.path.join(self.root_path, "results") + self.curves_result_path = os.path.join(self.root_path, "curves") + os.makedirs(self.result_path, exist_ok=True) + os.makedirs(self.curves_result_path, exist_ok=True) + self._prepare_market(benchmark_config, name, rebuild) + + @staticmethod + def _limited_data(method, test_info, loss_func): + all_scores = [] + for subset in test_info["train_subsets"]: + subset_scores = [] + for sample in subset: + x_train, y_train = sample["x_train"], sample["y_train"] + model = method(x_train, y_train, test_info) + subset_scores.append(loss_func(model.predict(test_info["test_x"]), test_info["test_y"])) + all_scores.append(np.mean(subset_scores)) + return all_scores + + @staticmethod + def get_train_subsets(train_x, train_y): + np.random.seed(1) + random.seed(1) + train_subsets = [] + for n_label, repeated in zip(n_labeled_list, n_repeat_list): + train_subsets.append([]) + if n_label > len(train_x): + n_label = len(train_x) + for _ in range(repeated): + x_train, y_train = zip(*random.sample(list(zip(train_x, train_y)), k=n_label)) + train_subsets[-1].append({"x_train": np.array(x_train), "y_train": np.array(list(y_train))}) + return train_subsets + + def _prepare_market(self, benchmark_config, name, rebuild): + client = LearnwareClient() + self.benchmark = LearnwareBenchmark().get_benchmark(benchmark_config) + self.market = instantiate_learnware_market(market_id=self.benchmark.name, name=name, rebuild=rebuild) + self.user_semantic = client.get_semantic_specification(self.benchmark.learnware_ids[0]) + self.user_semantic["Name"]["Values"] = "" + + if len(self.market) == 0 or rebuild == True: + for learnware_id in self.benchmark.learnware_ids: + with tempfile.TemporaryDirectory(prefix="table_benchmark_") as tempdir: + zip_path = os.path.join(tempdir, f"{learnware_id}.zip") + for i in range(20): + try: + semantic_spec = client.get_semantic_specification(learnware_id) + client.download_learnware(learnware_id, zip_path) + self.market.add_learnware(zip_path, semantic_spec) + break + except: + time.sleep(1) + continue + + def test_method(self, test_info, recorders, loss_func=loss_func_rmse): + method_name_full = test_info["method_name"] + method_name = method_name_full if method_name_full == "user_model" else "_".join(method_name_full.split("_")[1:]) + user, idx = test_info["user"], test_info["idx"] + recorder = recorders[method_name_full] + + save_root_path = os.path.join(self.curves_result_path, user, f"{user}_{idx}") + os.makedirs(save_root_path, exist_ok=True) + save_path = os.path.join(save_root_path, f"{method_name}.json") + + if method_name == "single_aug": + if test_info["force"] or recorder.should_test_method(user, idx, save_path): + for learnware in test_info["learnwares"]: + test_info["single_learnware"] = [learnware] + scores = self._limited_data(test_methods[method_name_full], test_info, loss_func) + recorder.record(user, idx, scores) + + process_single_aug(user, idx, scores, recorders, save_root_path) + recorder.save(save_path) + logger.info(f"Method {method_name} on {user}_{idx} finished") + else: + process_single_aug(user, idx, recorder.data[user][str(idx)], recorders, save_root_path) + logger.info(f"Method {method_name} on {user}_{idx} already exists") + else: + if test_info["force"] or recorder.should_test_method(user, idx, save_path): + scores = self._limited_data(test_methods[method_name_full], test_info, loss_func) + recorder.record(user, idx, scores) + recorder.save(save_path) + logger.info(f"Method {method_name} on {user}_{idx} finished") + else: + logger.info(f"Method {method_name} on {user}_{idx} already exists") \ No newline at end of file diff --git a/examples/dataset_table_workflow/config.py b/examples/dataset_table_workflow/config.py index c1e0ab8..14b60ed 100644 --- a/examples/dataset_table_workflow/config.py +++ b/examples/dataset_table_workflow/config.py @@ -1,3 +1,6 @@ +from learnware.tests.benchmarks import BenchmarkConfig + + n_labeled_list = [100, 200, 500, 1000, 2000, 4000, 6000, 8000, 10000] n_repeat_list = [10, 10, 10, 3, 3, 3, 3, 3, 3] @@ -16,30 +19,11 @@ labels = { 'user_model': "User Model", 'single_aug': "Single Learnware Reuse (Select)", "select_score": "Single Learnware Reuse (Select)", - # "Single Learnware Reuse (Avg)", - # "Single Learnware Reuse (Oracle)", 'multiple_aug': "Multiple Learnware Reuse (FeatAug)", 'ensemble_pruning': "Multiple Learnware Reuse (EnsemblePrune)", 'multiple_avg': "Multiple Learnware Reuse (Averaging)" } -output_description = { - "Dimension": 1, - "Description": { - "0": "Product sales on the date.", - }, -} - -user_semantic = { - "Data": {"Values": ["Table"], "Type": "Class"}, - "Task": {"Values": ["Regression"], "Type": "Class"}, - "Library": {"Values": ["Others"], "Type": "Class"}, - "Scenario": {"Values": ["Business"], "Type": "Tag"}, - "Description": {"Values": "", "Type": "String"}, - "Name": {"Values": "", "Type": "String"}, - "Output": output_description, -} - align_model_params = { "network_type": "ArbitraryMapping", # ["ArbitraryMapping", "BaseMapping", "BaseMapping_BN", "BaseMapping_Dropout"] "num_epoch": 50, @@ -63,3 +47,87 @@ market_mapping_params = { "ffn_dim": 512, # [128, 256, 512, 768, 1024], # the dimension of feed-forward layer in the transformer layer "activation": "leakyrelu", } + +user_model_params = { + "Corporacion": { + "lgb": { + "params": { + "num_leaves": 31, + "objective": "regression", + "learning_rate": 0.1, + "feature_fraction": 0.8, + "bagging_fraction": 0.8, + "bagging_freq": 2, + "metric": "l2", + "num_threads": 4, + "verbose": -1, + }, + "MAX_ROUNDS": 500, + "early_stopping_rounds": 50, + } + } +} + +homo_table_benchmark_config = BenchmarkConfig( + name="Corporacion", + user_num=54, + learnware_ids=[ + "00000912", + "00000911", + "00000910", + "00000909", + "00000908", + "00000907", + "00000906", + "00000905", + "00000904", + "00000903", + "00000902", + "00000901", + "00000900", + "00000899", + "00000898", + "00000897", + "00000896", + "00000895", + "00000894", + "00000893", + "00000892", + "00000891", + "00000890", + "00000889", + "00000888", + "00000887", + "00000886", + "00000885", + "00000884", + "00000883", + "00000882", + "00000881", + "00000880", + "00000879", + "00000878", + "00000877", + "00000876", + "00000875", + "00000874", + "00000873", + "00000872", + "00000871", + "00000870", + "00000869", + "00000868", + "00000867", + "00000866", + "00000865", + "00000864", + "00000863", + "00000862", + "00000861", + "00000860", + "00000859" + ], + test_data_path="Corporacion/test_data.zip", + train_data_path="Corporacion/train_data.zip", + extra_info_path="Corporacion/extra_info.zip", +) \ No newline at end of file diff --git a/examples/dataset_table_workflow/homo.py b/examples/dataset_table_workflow/homo.py index efa6deb..d6bb8df 100644 --- a/examples/dataset_table_workflow/homo.py +++ b/examples/dataset_table_workflow/homo.py @@ -1,204 +1,175 @@ import os import warnings -from collections import defaultdict - +import numpy as np warnings.filterwarnings("ignore") -import numpy as np -from matplotlib import pyplot as plt -from functools import partial -import learnware.specification as specification +from learnware.market import BaseUserInfo from learnware.logger import get_module_logger -from learnware.market import instantiate_learnware_market, BaseUserInfo +from learnware.specification import generate_stat_spec from learnware.reuse import AveragingReuser, JobSelectorReuser, EnsemblePruningReuser -from benchmarks import DataLoader -from base import TableWorkflow, user_semantic from methods import * -from config import n_labeled_list, n_repeat_list +from base import TableWorkflow +from config import n_labeled_list from utils import Recorder, plot_performance_curves -logger = get_module_logger("corporacion_test", level="INFO") -learnware_market = ["corporacion_bojan", "corporacion_lee", "corporacion_lingzhi"] -users = ["corporacion_lingzhi"] +logger = get_module_logger("homo_table", level="INFO") -class CorporacionDatasetWorkflow(TableWorkflow): - def __init__(self, reload_market=False, regenerate_flag=False): - super(CorporacionDatasetWorkflow, self).__init__(learnware_market) - self.curves_result_path = os.path.join(self.result_path, "curves") - self.figs_result_path = os.path.join(self.result_path, "figs") - - os.makedirs(self.curves_result_path, exist_ok=True) - os.makedirs(self.figs_result_path, exist_ok=True) - - if reload_market: - self.prepare_market(name="easy", market_id="corporacion", regenerate_flag=regenerate_flag) - - def test_homo_unlabeled(self): - corporacion_market = instantiate_learnware_market(market_id="corporacion") - logger.info("Total Item: %d" % len(corporacion_market)) - - learnware_rmse_list = defaultdict(list) - job_selector_score_list = defaultdict(list) - ensemble_score_list = defaultdict(list) - pruning_score_list = defaultdict(list) - - for user in learnware_market: - corporacion = DataLoader(user) - idx_list = corporacion.get_shop_ids() - for idx in idx_list: - _, _, test_x, test_y, _ = corporacion.get_raw_data(idx) - user_stat_spec = specification.RKMETableSpecification() - user_stat_spec.generate_stat_spec_from_data(X=test_x) - user_info = BaseUserInfo( - semantic_spec=user_semantic, stat_info={"RKMETableSpecification": user_stat_spec} - ) - logger.info(f"Searching Market for user: {user}_{idx}") - - search_result = corporacion_market.search_learnware(user_info, max_search_num=10) - single_result = search_result.get_single_results() - multiple_result = search_result.get_multiple_results() - - logger.info(f"search result of user {user}_{idx}:") - logger.info( - f"single model num: {len(single_result)}, max_score: {single_result[0].score}, min_score: {single_result[-1].score}" - ) - - l = len(single_result) - rmse_list = [] - for idx in range(l): - learnware = single_result[idx].learnware - pred_y = learnware.predict(test_x) - rmse_list.append(loss_func_mse(pred_y, test_y)) - logger.info( - f"Top1-score: {single_result[0].score}, learnware_id: {single_result[0].learnware.id}, rmse: {rmse_list[0]}" - ) - - if len(multiple_result) > 0: - mixture_id = " ".join([learnware.id for learnware in multiple_result[0].learnwares]) - logger.info(f"mixture_score: {multiple_result[0].score}, mixture_learnware: {mixture_id}") - mixture_learnware_list = multiple_result[0].learnwares - else: - mixture_learnware_list = [single_result[0].learnware] - - # test reuse (job selector) - reuse_baseline = JobSelectorReuser(learnware_list=mixture_learnware_list, herding_num=100) - reuse_predict = reuse_baseline.predict(user_data=test_x) - reuse_score = loss_func_mse(reuse_predict, test_y) - job_selector_score_list[user].append(reuse_score) - logger.info(f"mixture reuse rmse (job selector): {reuse_score}") - - # test reuse (ensemble) - reuse_ensemble = AveragingReuser(learnware_list=mixture_learnware_list, mode="mean") - ensemble_predict_y = reuse_ensemble.predict(user_data=test_x) - ensemble_score = loss_func_mse(ensemble_predict_y, test_y) - ensemble_score_list[user].append(ensemble_score) - logger.info(f"mixture reuse rmse (ensemble): {ensemble_score}") - - # test reuse (ensemblePruning) - reuse_pruning = EnsemblePruningReuser(learnware_list=mixture_learnware_list, mode="regression") - pruning_predict_y = reuse_pruning.predict(user_data=test_x) - pruning_score = loss_func_mse(pruning_predict_y, test_y) - pruning_score_list[user].append(pruning_score) - logger.info(f"mixture reuse rmse (ensemble Pruning): {pruning_score}\n") - - learnware_rmse_list[user].append(rmse_list) - - for user in learnware_market: - logger.info(f"User Dataset: {user}") - - single_list = np.array(learnware_rmse_list[user]) - select_score_list = [lst[0] for lst in single_list] - avg_score_list = [np.mean(lst, axis=0) for lst in single_list] - oracle_score_list = [np.min(lst, axis=0) for lst in single_list] - logger.info( - "RMSE of selected learnware: %.3f +/- %.3f, Average performance: %.3f +/- %.3f, Oracle performace: %.3f +/- %.3f" - % ( - np.mean(select_score_list), - np.std(select_score_list), - np.mean(avg_score_list), - np.std(avg_score_list), - np.mean(oracle_score_list), - np.std(oracle_score_list), - ) - ) - logger.info( - "Average Job Selector Reuse Performance: %.3f +/- %.3f" - % (np.mean(job_selector_score_list[user]), np.std(job_selector_score_list[user])) +class CorporacionDatasetWorkflow(TableWorkflow): + def unlabeled_homo_table_example(self): + logger.info("Total Item: %d" % (len(self.market))) + learnware_rmse_list = [] + single_score_list = [] + job_selector_score_list = [] + ensemble_score_list = [] + pruning_score_list = [] + all_learnwares = self.market.get_learnwares() + + user = self.benchmark.name + for idx in range(self.benchmark.user_num): + test_x, test_y = self.benchmark.get_test_data(user_ids=idx) + test_x, test_y = test_x.values, test_y.values + user_stat_spec = generate_stat_spec(type="table", X=test_x) + user_info = BaseUserInfo( + semantic_spec=self.user_semantic, stat_info={user_stat_spec.type: user_stat_spec} ) + logger.info(f"Searching Market for user: {user}_{idx}") + + search_result = self.market.search_learnware(user_info) + single_result = search_result.get_single_results() + multiple_result = search_result.get_multiple_results() + + logger.info(f"search result of user {user}_{idx}:") logger.info( - "Averaging Ensemble Reuse Performance: %.3f +/- %.3f" - % (np.mean(ensemble_score_list[user]), np.std(ensemble_score_list[user])) + f"single model num: {len(single_result)}, max_score: {single_result[0].score}, min_score: {single_result[-1].score}" ) + + pred_y = single_result[0].learnware.predict(test_x) + single_score_list.append(loss_func_rmse(pred_y, test_y)) + + rmse_list = [] + for learnware in all_learnwares: + pred_y = learnware.predict(test_x) + rmse_list.append(loss_func_rmse(pred_y, test_y)) logger.info( - "Selective Ensemble Reuse Performance: %.3f +/- %.3f" - % (np.mean(pruning_score_list[user]), np.std(pruning_score_list[user])) + f"Top1-score: {single_result[0].score}, learnware_id: {single_result[0].learnware.id}, rmse: {single_score_list[-1]}" ) - def test_homo_labeled(self): - corporacion_market = instantiate_learnware_market(market_id="corporacion") - logger.info("Total Item: %d" % len(corporacion_market)) - + if len(multiple_result) > 0: + mixture_id = " ".join([learnware.id for learnware in multiple_result[0].learnwares]) + logger.info(f"mixture_score: {multiple_result[0].score}, mixture_learnware: {mixture_id}") + mixture_learnware_list = multiple_result[0].learnwares + else: + mixture_learnware_list = [single_result[0].learnware] + + # test reuse (job selector) + reuse_baseline = JobSelectorReuser(learnware_list=mixture_learnware_list, herding_num=100) + reuse_predict = reuse_baseline.predict(user_data=test_x) + reuse_score = loss_func_rmse(reuse_predict, test_y) + job_selector_score_list.append(reuse_score) + logger.info(f"mixture reuse rmse (job selector): {reuse_score}") + + # test reuse (ensemble) + reuse_ensemble = AveragingReuser(learnware_list=mixture_learnware_list, mode="mean") + ensemble_predict_y = reuse_ensemble.predict(user_data=test_x) + ensemble_score = loss_func_rmse(ensemble_predict_y, test_y) + ensemble_score_list.append(ensemble_score) + logger.info(f"mixture reuse rmse (ensemble): {ensemble_score}") + + # test reuse (ensemblePruning) + reuse_pruning = EnsemblePruningReuser(learnware_list=mixture_learnware_list, mode="regression") + pruning_predict_y = reuse_pruning.predict(user_data=test_x) + pruning_score = loss_func_rmse(pruning_predict_y, test_y) + pruning_score_list.append(pruning_score) + logger.info(f"mixture reuse rmse (ensemble Pruning): {pruning_score}\n") + + learnware_rmse_list.append(rmse_list) + + single_list = np.array(learnware_rmse_list) + avg_score_list = [np.mean(lst, axis=0) for lst in single_list] + oracle_score_list = [np.min(lst, axis=0) for lst in single_list] + + logger.info( + "RMSE of selected learnware: %.3f +/- %.3f, Average performance: %.3f +/- %.3f, Oracle performace: %.3f +/- %.3f" + % ( + np.mean(single_score_list), + np.std(single_score_list), + np.mean(avg_score_list), + np.std(avg_score_list), + np.mean(oracle_score_list), + np.std(oracle_score_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)) + ) + + def labeled_homo_table_example(self): + logger.info("Total Item: %d" % (len(self.market))) methods = ["user_model", "homo_single_aug", "homo_multiple_aug", "homo_multiple_avg", "homo_ensemble_pruning"] recorders = {method: Recorder() for method in methods} - methods_to_retest = [] - for user in users: - data_loader = DataLoader(user) - idx_list = data_loader.get_shop_ids() - for idx in idx_list: - _, _, test_x, test_y, _ = data_loader.get_raw_data(idx) - train_subsets = data_loader.get_labeled_training_data( - idx, - size_list=n_labeled_list, - n_repeat_list=n_repeat_list - ) - - user_stat_spec = specification.RKMETableSpecification() - user_stat_spec.generate_stat_spec_from_data(X=test_x) - user_info = BaseUserInfo( - semantic_spec=user_semantic, stat_info={"RKMETableSpecification": user_stat_spec} - ) - logger.info(f"Searching Market for user: {user}_{idx}") - - search_result = corporacion_market.search_learnware(user_info, max_search_num=10) - single_result = search_result.get_single_results() - multiple_result = search_result.get_multiple_results() - - logger.info(f"search result of user {user}_{idx}:") - logger.info( - f"single model num: {len(single_result)}, max_score: {single_result[0].score}, min_score: {single_result[-1].score}" - ) - - if len(multiple_result) > 0: - mixture_id = " ".join([learnware.id for learnware in multiple_result[0].learnwares]) - logger.info(f"mixture_score: {multiple_result[0].score}, mixture_learnware: {mixture_id}") - mixture_learnware_list = multiple_result[0].learnwares - else: - mixture_learnware_list = [single_result[0].learnware] - - test_info = {"user": user, "idx": idx, "train_subsets": train_subsets, "test_x": test_x, "test_y": test_y} - common_config = {"multiple_learnwares": mixture_learnware_list} - method_configs = { - "user_model": {"data_loader": data_loader}, - "homo_single_aug": {"single_learnware": [single_result[0].learnware]}, - "homo_multiple_aug": common_config, - "homo_multiple_avg": common_config, - "homo_ensemble_pruning": common_config - } - - for method_name in methods: - # self.test_method(method_name, HeteroScoringMethods.__dict__[f"{method_name}_score"], recorders, test_info) - logger.info(f"Testing method {method_name}") - test_info["method_name"] = method_name - test_info["force"] = method_name in methods_to_retest - test_info.update(method_configs[method_name]) - self.test_method(test_info, recorders, loss_func=loss_func_mse) + user = self.benchmark.name + for idx in range(self.benchmark.user_num): + test_x, test_y = self.benchmark.get_test_data(user_ids=idx) + test_x, test_y = test_x.values, test_y.values - for method, recorder in recorders.items(): - recorder.save(os.path.join(self.curves_result_path, f"{user}_{method}_performance.json")) - - methods_to_plot = ["user_model", "homo_single_aug", "homo_ensemble_pruning"] - plot_performance_curves(user, {method: recorders[method] for method in methods_to_plot}, task="Homo", n_labeled_list=n_labeled_list) \ No newline at end of file + train_x, train_y = self.benchmark.get_train_data(user_ids=idx) + train_x, train_y = train_x.values, train_y.values + train_subsets = self.get_train_subsets(train_x, train_y) + + user_stat_spec = generate_stat_spec(type="table", X=test_x) + user_info = BaseUserInfo( + semantic_spec=self.user_semantic, stat_info={"RKMETableSpecification": user_stat_spec} + ) + logger.info(f"Searching Market for user: {user}_{idx}") + + search_result = self.market.search_learnware(user_info) + single_result = search_result.get_single_results() + multiple_result = search_result.get_multiple_results() + + logger.info(f"search result of user {user}_{idx}:") + logger.info( + f"single model num: {len(single_result)}, max_score: {single_result[0].score}, min_score: {single_result[-1].score}" + ) + + if len(multiple_result) > 0: + mixture_id = " ".join([learnware.id for learnware in multiple_result[0].learnwares]) + logger.info(f"mixture_score: {multiple_result[0].score}, mixture_learnware: {mixture_id}") + mixture_learnware_list = multiple_result[0].learnwares + else: + mixture_learnware_list = [single_result[0].learnware] + + test_info = {"user": user, "idx": idx, "train_subsets": train_subsets, "test_x": test_x, "test_y": test_y} + common_config = {"learnwares": mixture_learnware_list} + method_configs = { + "user_model": {"dataset": self.benchmark.name, "model_type": "lgb"}, + "homo_single_aug": {"learnwares": [single_result[0].learnware]}, + "homo_multiple_aug": common_config, + "homo_multiple_avg": common_config, + "homo_ensemble_pruning": common_config + } + + for method_name in methods: + logger.info(f"Testing method {method_name}") + test_info["method_name"] = method_name + test_info["force"] = method_name in methods_to_retest + test_info.update(method_configs[method_name]) + self.test_method(test_info, recorders, loss_func=loss_func_rmse) + + for method, recorder in recorders.items(): + recorder.save(os.path.join(self.curves_result_path, f"{user}_{method}_performance.json")) + + methods_to_plot = ["user_model", "homo_single_aug", "homo_ensemble_pruning"] + plot_performance_curves(user, {method: recorders[method] for method in methods_to_plot}, task="Homo", n_labeled_list=n_labeled_list) \ No newline at end of file diff --git a/examples/dataset_table_workflow/main.py b/examples/dataset_table_workflow/main.py deleted file mode 100644 index 19911b8..0000000 --- a/examples/dataset_table_workflow/main.py +++ /dev/null @@ -1,34 +0,0 @@ -import fire -from pyinstrument import Profiler - -from dataset_corporacion_workflow import CorporacionDatasetWorkflow -from dataset_heterogeneous_workflow import HeterogeneousWorkflow - -workflow_mapping = { - "test_homo_unlabeled": CorporacionDatasetWorkflow, - "test_homo_labeled": CorporacionDatasetWorkflow, - "test_hetero_unlabeled": HeterogeneousWorkflow, - "test_hetero_labeled": HeterogeneousWorkflow, -} - - -def main(): - def dispatch(command): - if command in workflow_mapping: - workflow_class = workflow_mapping[command] - workflow_instance = workflow_class() - getattr(workflow_instance, command)() - else: - print(f"No workflow found for command: {command}") - - fire.Fire(dispatch) - - -if __name__ == "__main__": - profiler = Profiler() - profiler.start() - - main() - - profiler.stop() - profiler.print() diff --git a/examples/dataset_table_workflow/methods.py b/examples/dataset_table_workflow/methods.py index 11580fb..fe40eef 100644 --- a/examples/dataset_table_workflow/methods.py +++ b/examples/dataset_table_workflow/methods.py @@ -1,23 +1,22 @@ import numpy as np from sklearn.metrics import mean_squared_error -from sklearn.model_selection import train_test_split # Add missing import -from loguru import logger +from sklearn.model_selection import train_test_split from learnware.reuse import AveragingReuser, EnsemblePruningReuser, FeatureAugmentReuser, HeteroMapAlignLearnware -from examples.dataset_table_workflow.config import align_model_params +from config import align_model_params +from train import train_model + def loss_func_rmse(y_true, y_pred): return np.sqrt(mean_squared_error(y_true, y_pred)) -def loss_func_mse(y_true, y_pred): - return mean_squared_error(y_true, y_pred) def user_model_score(x_train, y_train, test_info): - data_loader = test_info["data_loader"] x_train, x_val, y_train, y_val = train_test_split(x_train, y_train, test_size=0.2, random_state=42) - user_model = data_loader.train_a_model(x_train, y_train, x_val, y_val) + user_model = train_model(x_train, y_train, x_val, y_val, test_info) return user_model + class HomoScoringMethods: @staticmethod def single_aug_score(x_train, y_train, test_info): @@ -28,20 +27,20 @@ class HomoScoringMethods: @staticmethod def multiple_aug_score(x_train, y_train, test_info): - multiple_learnwares = test_info["multiple_learnwares"] + multiple_learnwares = test_info["learnwares"] reuse_multiple_augment = FeatureAugmentReuser(multiple_learnwares, mode="regression") reuse_multiple_augment.fit(x_train=x_train, y_train=y_train) return reuse_multiple_augment @staticmethod def multiple_avg_score(x_train, y_train, test_info): - multiple_learnwares = test_info["multiple_learnwares"] + multiple_learnwares = test_info["learnwares"] reuse_multiple_avg = AveragingReuser(multiple_learnwares, mode="mean") return reuse_multiple_avg @staticmethod def multiple_ensemble_pruning_score(x_train, y_train, test_info): - multiple_learnwares = test_info["multiple_learnwares"] + multiple_learnwares = test_info["learnwares"] if len(multiple_learnwares) == 1: return multiple_learnwares[0] reuse_pruning = EnsemblePruningReuser(multiple_learnwares, mode="regression") @@ -51,7 +50,7 @@ class HomoScoringMethods: class HeteroMethods: @staticmethod - def create_hetero_learnware_list(learnware_list, user_rkme, x_train, y_train): # Fix typo in method name + def create_hetero_learnware_list(learnware_list, user_rkme, x_train, y_train): hetero_learnware_list = [] for learnware in learnware_list: hetero_learnware = HeteroMapAlignLearnware(learnware, mode="regression", **align_model_params) @@ -68,7 +67,7 @@ class HeteroMethods: @staticmethod def multiple_aug_score(x_train, y_train, test_info): - user_rkme, multiple_learnwares = test_info["user_rkme"], test_info["multiple_learnwares"] + user_rkme, multiple_learnwares = test_info["user_rkme"], test_info["learnwares"] hetero_learnware_list = HeteroMethods.create_hetero_learnware_list(multiple_learnwares, user_rkme, x_train, y_train) reuse_multiple_augment = FeatureAugmentReuser(hetero_learnware_list, mode="regression") reuse_multiple_augment.fit(x_train=x_train, y_train=y_train) @@ -76,7 +75,7 @@ class HeteroMethods: @staticmethod def multiple_ensemble_pruning_score(x_train, y_train, test_info): - user_rkme, multiple_learnwares = test_info["user_rkme"], test_info["multiple_learnwares"] + user_rkme, multiple_learnwares = test_info["user_rkme"], test_info["learnwares"] hetero_learnware_list = HeteroMethods.create_hetero_learnware_list(multiple_learnwares, user_rkme, x_train, y_train) if len(hetero_learnware_list) == 1: return hetero_learnware_list[0] @@ -86,7 +85,7 @@ class HeteroMethods: @staticmethod def multiple_avg_score(x_train, y_train, test_info): - user_rkme, multiple_learnwares = test_info["user_rkme"], test_info["multiple_learnwares"] + user_rkme, multiple_learnwares = test_info["user_rkme"], test_info["learnwares"] hetero_learnware_list = HeteroMethods.create_hetero_learnware_list(multiple_learnwares, user_rkme, x_train, y_train) reuse_multiple_avg = AveragingReuser(hetero_learnware_list, mode="mean") return reuse_multiple_avg diff --git a/examples/dataset_table_workflow/train.py b/examples/dataset_table_workflow/train.py new file mode 100644 index 0000000..7ac2b3a --- /dev/null +++ b/examples/dataset_table_workflow/train.py @@ -0,0 +1,48 @@ +import numpy as np +import lightgbm as lgb +from lightgbm import early_stopping +from sklearn.metrics import mean_squared_error + +from learnware.logger import get_module_logger +from config import user_model_params + +logger = get_module_logger("train_table", level="INFO") + + +def train_lgb(X_train, y_train, X_val, y_val, dataset): + logger.info("Training and predicting models...") + model_param = user_model_params[dataset]["lgb"] + params = model_param["params"] + + MAX_ROUNDS = model_param["MAX_ROUNDS"] + val_pred = [] + cate_vars = [] + + logger.info(f"{np.shape(X_train)}, {np.shape(y_train)}, {np.shape(X_val)}, {np.shape(y_val)}") + + dtrain = lgb.Dataset(X_train, label=y_train, categorical_feature=cate_vars) + dval = lgb.Dataset(X_val, label=y_val, reference=dtrain, categorical_feature=cate_vars) + bst = lgb.train( + params, + dtrain, + num_boost_round=MAX_ROUNDS, + valid_sets=[dtrain, dval], + callbacks=[early_stopping(model_param["early_stopping_rounds"], verbose=False)] + ) + val_pred.append(bst.predict(X_val, num_iteration=bst.best_iteration or MAX_ROUNDS)) + logger.info(f"Validation mse:{mean_squared_error(y_val, np.array(val_pred).transpose())}") + + return bst + + +def train_ridge(X_train, y_train, X_val, y_val, dataset): + pass + + +def train_model(X_train, y_train, X_val, y_val, test_info): + dataset = test_info["dataset"] + model_type = test_info["model_type"] + assert model_type in ["lgb", "ridge"] + + if model_type == "lgb": + return train_lgb(X_train, y_train, X_val, y_val, dataset) \ No newline at end of file diff --git a/examples/dataset_table_workflow/utils.py b/examples/dataset_table_workflow/utils.py index d37e4bd..c467b9b 100644 --- a/examples/dataset_table_workflow/utils.py +++ b/examples/dataset_table_workflow/utils.py @@ -1,14 +1,15 @@ import os -from collections import defaultdict - import json -import matplotlib.pyplot as plt -import numpy as np -from loguru import logger import traceback +import numpy as np +import matplotlib.pyplot as plt +from collections import defaultdict + +from learnware.logger import get_module_logger +from config import * + +logger = get_module_logger("base_table", level="INFO") -from examples.dataset_table_workflow.config import * -from benchmarks.config import default_size_list class Recorder: def __init__(self, headers=["Mean", "Std Dev"], formats=["{:.2f}", "{:.2f}"]): @@ -79,7 +80,7 @@ def analyze_performance(user, recorders): logger.info(f"{user}, {user_id}, {mean_differences[user_id]}, {single_multi_diff}") -def plot_performance_curves(user, recorders, task="Hetero", n_labeled_list=default_size_list): +def plot_performance_curves(user, recorders, task, n_labeled_list): plt.figure(figsize=(10, 6)) for method, recorder in recorders.items(): @@ -106,4 +107,8 @@ def plot_performance_curves(user, recorders, task="Hetero", n_labeled_list=defau plt.title(f'Table {task} Limited Labeled Data') plt.legend() plt.tight_layout() - plt.savefig(os.path.join('./results/figs', f"{user}_labeled_{list(recorders.keys())}.png"), bbox_inches="tight", dpi=700) \ No newline at end of file + + root_path = os.path.abspath(os.path.join(__file__, "..")) + fig_path = os.path.join(root_path, "results", "figs") + os.makedirs(fig_path, exist_ok=True) + plt.savefig(os.path.join(fig_path, f"{user}_labeled_{list(recorders.keys())}.svg"), bbox_inches="tight", dpi=700) \ No newline at end of file diff --git a/examples/dataset_table_workflow/workflow.py b/examples/dataset_table_workflow/workflow.py new file mode 100644 index 0000000..3f0a319 --- /dev/null +++ b/examples/dataset_table_workflow/workflow.py @@ -0,0 +1,35 @@ +import fire + +from learnware.logger import get_module_logger +from homo import CorporacionDatasetWorkflow +from config import homo_table_benchmark_config + +logger = get_module_logger("base_table", level="INFO") + + +class TableDatasetWorkflow: + def unlabeled_homo_table_example(self): + workflow = CorporacionDatasetWorkflow( + benchmark_config=homo_table_benchmark_config, + name="easy", + rebuild=False + ) + workflow.unlabeled_homo_table_example() + + def labeled_homo_table_example(self): + workflow = CorporacionDatasetWorkflow( + benchmark_config=homo_table_benchmark_config, + name="easy", + rebuild=False + ) + workflow.labeled_homo_table_example() + + def cross_feat_eng_hetero_table_example(self): + pass + + def cross_task_hetero_table_example(self): + pass + + +if __name__ == "__main__": + fire.Fire(TableDatasetWorkflow) \ No newline at end of file From cf2b64d1b7f31208d77fa63cf8448d90e501f5ec Mon Sep 17 00:00:00 2001 From: liuht Date: Wed, 27 Dec 2023 23:39:52 +0800 Subject: [PATCH 04/32] [MNT] unify figure format --- .gitignore | 3 +- examples/dataset_table_workflow/base.py | 23 ++++----- examples/dataset_table_workflow/homo.py | 6 +-- examples/dataset_table_workflow/utils.py | 59 +++++++++++++----------- 4 files changed, 48 insertions(+), 43 deletions(-) diff --git a/.gitignore b/.gitignore index d8c41d5..f361d74 100644 --- a/.gitignore +++ b/.gitignore @@ -44,4 +44,5 @@ tmp/ learnware_pool/ PFS/ data/ -examples/results/ \ No newline at end of file +examples/results/ +examples/*/results/ \ No newline at end of file diff --git a/examples/dataset_table_workflow/base.py b/examples/dataset_table_workflow/base.py index fe31bd6..39924c8 100644 --- a/examples/dataset_table_workflow/base.py +++ b/examples/dataset_table_workflow/base.py @@ -34,13 +34,13 @@ class TableWorkflow: x_train, y_train = sample["x_train"], sample["y_train"] model = method(x_train, y_train, test_info) subset_scores.append(loss_func(model.predict(test_info["test_x"]), test_info["test_y"])) - all_scores.append(np.mean(subset_scores)) + all_scores.append(subset_scores) return all_scores @staticmethod - def get_train_subsets(train_x, train_y): - np.random.seed(1) - random.seed(1) + def get_train_subsets(idx, train_x, train_y): + np.random.seed(idx) + random.seed(idx) train_subsets = [] for n_label, repeated in zip(n_labeled_list, n_repeat_list): train_subsets.append([]) @@ -82,24 +82,21 @@ class TableWorkflow: os.makedirs(save_root_path, exist_ok=True) save_path = os.path.join(save_root_path, f"{method_name}.json") - if method_name == "single_aug": + if method_name_full == "hetero_single_aug": if test_info["force"] or recorder.should_test_method(user, idx, save_path): for learnware in test_info["learnwares"]: test_info["single_learnware"] = [learnware] scores = self._limited_data(test_methods[method_name_full], test_info, loss_func) - recorder.record(user, idx, scores) + recorder.record(user, scores) process_single_aug(user, idx, scores, recorders, save_root_path) recorder.save(save_path) - logger.info(f"Method {method_name} on {user}_{idx} finished") else: - process_single_aug(user, idx, recorder.data[user][str(idx)], recorders, save_root_path) - logger.info(f"Method {method_name} on {user}_{idx} already exists") + process_single_aug(user, idx, recorder.data[user], recorders, save_root_path) else: if test_info["force"] or recorder.should_test_method(user, idx, save_path): scores = self._limited_data(test_methods[method_name_full], test_info, loss_func) - recorder.record(user, idx, scores) + recorder.record(user, scores) recorder.save(save_path) - logger.info(f"Method {method_name} on {user}_{idx} finished") - else: - logger.info(f"Method {method_name} on {user}_{idx} already exists") \ No newline at end of file + + logger.info(f"Method {method_name} on {user}_{idx} finished") \ No newline at end of file diff --git a/examples/dataset_table_workflow/homo.py b/examples/dataset_table_workflow/homo.py index d6bb8df..03e45a9 100644 --- a/examples/dataset_table_workflow/homo.py +++ b/examples/dataset_table_workflow/homo.py @@ -117,8 +117,8 @@ class CorporacionDatasetWorkflow(TableWorkflow): def labeled_homo_table_example(self): logger.info("Total Item: %d" % (len(self.market))) methods = ["user_model", "homo_single_aug", "homo_multiple_aug", "homo_multiple_avg", "homo_ensemble_pruning"] - recorders = {method: Recorder() for method in methods} methods_to_retest = [] + recorders = {method: Recorder() for method in methods} user = self.benchmark.name for idx in range(self.benchmark.user_num): @@ -127,7 +127,7 @@ class CorporacionDatasetWorkflow(TableWorkflow): train_x, train_y = self.benchmark.get_train_data(user_ids=idx) train_x, train_y = train_x.values, train_y.values - train_subsets = self.get_train_subsets(train_x, train_y) + train_subsets = self.get_train_subsets(idx, train_x, train_y) user_stat_spec = generate_stat_spec(type="table", X=test_x) user_info = BaseUserInfo( @@ -155,7 +155,7 @@ class CorporacionDatasetWorkflow(TableWorkflow): common_config = {"learnwares": mixture_learnware_list} method_configs = { "user_model": {"dataset": self.benchmark.name, "model_type": "lgb"}, - "homo_single_aug": {"learnwares": [single_result[0].learnware]}, + "homo_single_aug": {"single_learnware": [single_result[0].learnware]}, "homo_multiple_aug": common_config, "homo_multiple_avg": common_config, "homo_ensemble_pruning": common_config diff --git a/examples/dataset_table_workflow/utils.py b/examples/dataset_table_workflow/utils.py index c467b9b..fd31064 100644 --- a/examples/dataset_table_workflow/utils.py +++ b/examples/dataset_table_workflow/utils.py @@ -14,18 +14,15 @@ logger = get_module_logger("base_table", level="INFO") class Recorder: def __init__(self, headers=["Mean", "Std Dev"], formats=["{:.2f}", "{:.2f}"]): assert len(headers) == len(formats), "Headers and formats length must match." - self.data = defaultdict(lambda: defaultdict(list)) + self.data = defaultdict(list) self.headers = headers self.formats = formats - def record(self, user, idx, scores): - self.data[user][idx].append(scores) + def record(self, user, scores): + self.data[user].append(scores) def get_performance_data(self, user): - if user in self.data: - return [idx_scores for idx_scores in self.data[user].values()] - else: - return [] + return self.data.get(user, []) def save(self, path): with open(path, "w") as f: @@ -38,7 +35,7 @@ class Recorder: def should_test_method(self, user, idx, path): if os.path.exists(path): self.load(path) - return user not in self.data or str(idx) not in self.data[user] + return user not in self.data or idx > len(self.data[user]) - 1 return True @@ -53,7 +50,7 @@ def process_single_aug(user, idx, scores, recorders, root_path): for method_name, scores in zip(["select_score", "mean_score", "oracle_score"], [select_scores, mean_scores, oracle_scores]): - recorders[method_name].record(user, idx, scores) + recorders[method_name].record(user, scores) save_path = os.path.join(root_path, f"{method_name}_performance.json") recorders[method_name].save(save_path) except Exception as e: @@ -82,33 +79,43 @@ def analyze_performance(user, recorders): def plot_performance_curves(user, recorders, task, n_labeled_list): plt.figure(figsize=(10, 6)) + plt.xticks(range(len(n_labeled_list)), n_labeled_list) for method, recorder in recorders.items(): if method == "hetero_single_aug": continue - user_data = recorder.get_performance_data(user) - - if user_data: - scores_array = np.array([np.array(lst) for lst in user_data]) - mean_scores = np.squeeze(np.mean(scores_array, axis=0)) - std_scores = np.squeeze(np.std(scores_array, axis=0)) + scores_array = recorder.get_performance_data(user) + if scores_array: + mean_curve, std_curve = [], [] + for i in range(len(n_labeled_list)): + sub_scores_array = np.vstack([lst[i] for lst in scores_array]) + sub_scores_mean = np.squeeze(np.mean(sub_scores_array, axis=0)) + mean_curve.append(np.mean(sub_scores_mean)) + std_curve.append(np.std(sub_scores_mean)) + + mean_curve = np.array(mean_curve) + std_curve = np.array(std_curve) method_plot = '_'.join(method.split('_')[1:]) if method not in ['user_model', 'oracle_score', 'select_score', 'mean_score'] else method style = styles.get(method_plot, {"color": "black", "linestyle": "-"}) - plt.plot(range(len(n_labeled_list)), mean_scores, label=labels.get(method_plot), **style) - - std_scale = 0.2 if task == "Hetero" else 0.5 - plt.fill_between(range(len(n_labeled_list)), mean_scores - std_scale * std_scores, mean_scores + std_scale * std_scores, color=style["color"], alpha=0.2) - - plt.xticks(range(len(n_labeled_list)), n_labeled_list) - plt.xlabel('Sample Size') - plt.ylabel('RMSE') - plt.title(f'Table {task} Limited Labeled Data') - plt.legend() + plt.plot(mean_curve, label=labels.get(method_plot), **style) + + plt.fill_between( + range(len(mean_curve)), + mean_curve - std_curve, + mean_curve + std_curve, + color=style["color"], + alpha=0.2 + ) + + plt.xlabel("Amount of Labeled User Data", fontsize=14) + plt.ylabel("RMSE", fontsize=14) + plt.title(f"Results on Homo Table Experimental Scenario", fontsize=16) + plt.legend(fontsize=14) plt.tight_layout() root_path = os.path.abspath(os.path.join(__file__, "..")) fig_path = os.path.join(root_path, "results", "figs") os.makedirs(fig_path, exist_ok=True) - plt.savefig(os.path.join(fig_path, f"{user}_labeled_{list(recorders.keys())}.svg"), bbox_inches="tight", dpi=700) \ No newline at end of file + plt.savefig(os.path.join(fig_path, f"{task}_labeled_curves.svg"), bbox_inches="tight", dpi=700) \ No newline at end of file From 82e1aa212ebe702d4e59db1f5acbd86b29310f79 Mon Sep 17 00:00:00 2001 From: liuht Date: Wed, 3 Jan 2024 13:27:07 +0800 Subject: [PATCH 05/32] [MNT] init hetero table workflow --- examples/dataset_table_workflow/base.py | 58 +++- examples/dataset_table_workflow/hetero.py | 339 +++++++++------------- 2 files changed, 195 insertions(+), 202 deletions(-) diff --git a/examples/dataset_table_workflow/base.py b/examples/dataset_table_workflow/base.py index 39924c8..afe79b0 100644 --- a/examples/dataset_table_workflow/base.py +++ b/examples/dataset_table_workflow/base.py @@ -1,13 +1,16 @@ import os import time -import pandas +import torch import random import tempfile import numpy as np +from queue import Empty +from tqdm import tqdm from learnware.client import LearnwareClient from learnware.logger import get_module_logger from learnware.market import instantiate_learnware_market from learnware.tests.benchmarks import LearnwareBenchmark +from torch.multiprocessing import Process, Queue, set_start_method from config import * from methods import * @@ -15,6 +18,12 @@ from utils import process_single_aug logger = get_module_logger("base_table", level="INFO") +try: + set_start_method('spawn') +except RuntimeError: + pass +torch.multiprocessing.set_sharing_strategy("file_system") + class TableWorkflow: def __init__(self, benchmark_config, name="easy", rebuild=False): @@ -24,6 +33,8 @@ class TableWorkflow: os.makedirs(self.result_path, exist_ok=True) os.makedirs(self.curves_result_path, exist_ok=True) self._prepare_market(benchmark_config, name, rebuild) + + self.cuda_idx = list(range(torch.cuda.device_count())) @staticmethod def _limited_data(method, test_info, loss_func): @@ -75,6 +86,7 @@ class TableWorkflow: def test_method(self, test_info, recorders, loss_func=loss_func_rmse): method_name_full = test_info["method_name"] method_name = method_name_full if method_name_full == "user_model" else "_".join(method_name_full.split("_")[1:]) + method = test_methods[method_name_full] user, idx = test_info["user"], test_info["idx"] recorder = recorders[method_name_full] @@ -84,15 +96,45 @@ class TableWorkflow: if method_name_full == "hetero_single_aug": if test_info["force"] or recorder.should_test_method(user, idx, save_path): - for learnware in test_info["learnwares"]: - test_info["single_learnware"] = [learnware] - scores = self._limited_data(test_methods[method_name_full], test_info, loss_func) - recorder.record(user, scores) + # # single process testing + # all_scores = [] + # for learnware in test_info["learnwares"]: + # test_info["single_learnware"] = [learnware] + # all_scores.append(self._limited_data(test_methods[method_name_full], test_info, loss_func)) + # recorder.record(user, all_scores) + + # multi process testing + queue = Queue() + processes = [] + bar = tqdm(total=len(test_info["learnwares"]), desc=f"Test {method_name}", unit="learnware") + learnware_chunks = [test_info["learnwares"][i:len(test_info["learnwares"]):len(self.cuda_idx)] for i in self.cuda_idx] + + for cuda_idx, learnware_chunk in zip(self.cuda_idx, learnware_chunks): + p = Process(target=self.process_learnware_chunk, args=(cuda_idx, method, test_info, loss_func, learnware_chunk, queue)) + processes.append(p) + p.start() + + all_results = [] + while any(p.is_alive() for p in processes) or not queue.empty(): + try: + result = queue.get(timeout=0.1) + all_results.append(result) + bar.update(1) + except Empty: + time.sleep(0.1) + continue + bar.close() - process_single_aug(user, idx, scores, recorders, save_root_path) + for p in processes: + p.join() + + all_results.sort(key=lambda x: x[0]) + all_scores = [result[1] for result in all_results] + recorder.record(user, all_scores) + # process_single_aug(user, idx, all_scores, recorders, save_root_path) recorder.save(save_path) - else: - process_single_aug(user, idx, recorder.data[user], recorders, save_root_path) + # else: + process_single_aug(user, idx, recorder.data[user][idx], recorders, save_root_path) else: if test_info["force"] or recorder.should_test_method(user, idx, save_path): scores = self._limited_data(test_methods[method_name_full], test_info, loss_func) diff --git a/examples/dataset_table_workflow/hetero.py b/examples/dataset_table_workflow/hetero.py index 267a2b1..246fd06 100644 --- a/examples/dataset_table_workflow/hetero.py +++ b/examples/dataset_table_workflow/hetero.py @@ -6,220 +6,171 @@ warnings.filterwarnings("ignore") import json import numpy as np -from matplotlib import pyplot as plt -import learnware.specification as specification from learnware.logger import get_module_logger +from learnware.specification import generate_stat_spec from learnware.market import instantiate_learnware_market, BaseUserInfo -from learnware.reuse import AveragingReuser, FeatureAlignLearnware -from multiprocessing import Pool +from learnware.reuse import AveragingReuser, JobSelectorReuser, FeatureAlignLearnware -from benchmarks import DataLoader -from examples.dataset_table_workflow.methods import * -from base import TableWorkflow, user_semantic +from methods import * +from base import TableWorkflow from config import align_model_params -from utils import Recorder, plot_performance_curves, analyze_performance +from utils import Recorder, plot_performance_curves logger = get_module_logger("hetero_test", level="INFO") learnware_market = ["pfs_default", "pfs_denis", "corporacion_bojan", "corporacion_lee", "corporacion_lingzhi"] default_users = ["m5_default"] # "m5_default", "m5_kkiller", "m5_rana" -n_labeled_list = [100, 200, 500, 1000, 2000] -n_repeat_list = [10, 10, 10, 3, 3] - -class HeterogeneousWorkflow(TableWorkflow): - def __init__(self, reload_market=False, regenerate_flag=False): - super(HeterogeneousWorkflow, self).__init__(learnware_market) - self.curves_result_path = os.path.join(self.result_path, 'curves') - self.unlabeled_res_path = os.path.join(self.result_path, 'unlabeled') - self.figs_result_path = os.path.join(self.result_path, "figs") - - os.makedirs(self.curves_result_path, exist_ok=True) - os.makedirs(self.unlabeled_res_path, exist_ok=True) - os.makedirs(self.figs_result_path, exist_ok=True) - - if reload_market: - self.prepare_market(name="hetero", market_id="heterogeneous", regenerate_flag=regenerate_flag) - - def test_hetero_unlabeled(self): - hetero_market = instantiate_learnware_market(market_id="heterogeneous", name="hetero") - logger.info("Total Item: %d" % len(hetero_market)) +n_labeled_list = [50, 75, 100, 200] +n_repeat_list = [10, 10, 10, 10] + +class HeterogeneousDatasetWorkflow(TableWorkflow): + def unlabeled_hetero_table_example(self): + logger.info("Total Item: %d" % len(self.market)) + learnware_rmse_list = [] + single_score_list = [] + job_selector_score_list = [] + ensemble_score_list = [] + all_learnwares = self.market.get_learnwares() - select_list = defaultdict(list) - avg_list = defaultdict(list) - oracle_list = defaultdict(list) - improve_list = defaultdict(list) - ensemble_score_list = defaultdict(list) - user_model_score_lists = defaultdict(list) - - user_model_method = partial(self._limited_data, HeteroMethods.user_model_score) - - for user in default_users: - user_unlabeld_res_path = os.path.join(self.unlabeled_res_path, f"{user}.json") - if os.path.exists(user_unlabeld_res_path): - with open(user_unlabeld_res_path, "rb") as file: - unlabeld_res = json.load(file) - select_list[user] = unlabeld_res["select_list"] - avg_list[user] = unlabeld_res["avg_list"] - oracle_list[user] = unlabeld_res["oracle_list"] - ensemble_score_list[user] = unlabeld_res["ensemble_score_list"] - user_model_score_lists[user] = unlabeld_res["user_model_score_lists"] - else: - data_loader = DataLoader(user) - idx_list = data_loader.get_shop_ids() - for idx in idx_list: - _, _, test_x, test_y, feature_descriptions = data_loader.get_raw_data(idx) - train_x_list, _ = data_loader.get_labeled_training_data(idx, size_list=n_labeled_list, n_repeat_list=n_repeat_list) - user_stat_spec = specification.RKMETableSpecification() - user_stat_spec.generate_stat_spec_from_data(X=test_x) - - feature_dim = len(feature_descriptions) - feature_descriptions_dict = {str(i): feature_descriptions[i] for i in range(feature_dim)} - input_description = { - "Dimension": feature_dim, - "Description": feature_descriptions_dict - } - user_semantic["Input"] = input_description - user_info = BaseUserInfo(semantic_spec=user_semantic, stat_info={"RKMETableSpecification": user_stat_spec}) - logger.info(f"Searching Market for user: {user}_{idx}") - - search_result = hetero_market.search_learnware(user_info) - single_result = search_result.get_single_results() - multiple_result = search_result.get_multiple_results() - - logger.info(f"hetero search result of user {user}_{idx}:") - logger.info( - f"single model num: {len(single_result)}, max_score: {single_result[0].score}, min_score: {single_result[-1].score}" - ) - - l = len(single_result) - rmse_list = [] - for idx in range(l): - hetero_learnware = FeatureAlignLearnware(single_result[idx].learnware, **align_model_params) - hetero_learnware.align(user_rkme=user_stat_spec) - pred_y = hetero_learnware.predict(test_x) - rmse_list.append(loss_func_rmse(pred_y, test_y)) - - logger.info( - f"Top1-score: {single_result[0].score}, learnware_id: {single_result[0].learnware.id}, rmse: {rmse_list[0]}" - ) - - if len(multiple_result) > 0: - mixture_id = " ".join([learnware.id for learnware in multiple_result[0].learnwares]) - logger.info(f"mixture_score: {multiple_result[0].score}, mixture_learnware: {mixture_id}") - mixture_learnware_list = [] - for learnware in multiple_result[0].learnwares: - hetero_learnware = FeatureAlignLearnware(learnware, **align_model_params) - hetero_learnware.align(user_rkme=user_stat_spec) - mixture_learnware_list.append(hetero_learnware) - else: - hetero_learnware = FeatureAlignLearnware(single_result[0].learnware, **align_model_params) - hetero_learnware.align(user_rkme=user_stat_spec) - mixture_learnware_list = [hetero_learnware] - - # test user model - user_model_score_list = user_model_method(train_x_list, test_x, test_y, data_loader) - - # test reuse (ensemble) - reuse_ensemble = AveragingReuser(learnware_list=mixture_learnware_list, mode="mean") - ensemble_predict_y = reuse_ensemble.predict(user_data=test_x) - ensemble_score = loss_func_rmse(ensemble_predict_y, test_y) - ensemble_score_list[user].append(ensemble_score) - logger.info(f"mixture reuse rmse (ensemble): {ensemble_score}") - - select_list[user].append(rmse_list[0]) - avg_list[user].append(np.mean(rmse_list)) - oracle_list[user].append(np.min(rmse_list)) - improve_list[user].append((np.mean(rmse_list) - rmse_list[0]) / np.mean(rmse_list)) - user_model_score_lists[user].append(user_model_score_list) - - logger.info(f"Saving unlabeled results for User: {user}") - res = { - "select_list": select_list[user], - "avg_list": avg_list[user], - "oracle_list": oracle_list[user], - "ensemble_score_list": ensemble_score_list[user], - "user_model_score_lists": user_model_score_lists[user] - } - with open(user_unlabeld_res_path, "w") as file: - json.dump(res, file, indent=4) - - for user in default_users: - logger.info(f"User Dataset: {user}") + user = self.benchmark.name + for idx in range(self.benchmark.user_num): + test_x, test_y = self.benchmark.get_test_data(user_ids=idx) + test_x, test_y = test_x.values, test_y.values + user_stat_spec = generate_stat_spec(type="table", X=test_x) + user_info = BaseUserInfo( + semantic_spec=self.user_semantic, stat_info={user_stat_spec.type: user_stat_spec} + ) + logger.info(f"Searching Market for user: {user}_{idx}") + + search_result = self.market.search_learnware(user_info) + single_result = search_result.get_single_results() + multiple_result = search_result.get_multiple_results() + + logger.info(f"hetero search result of user {user}_{idx}:") logger.info( - "RMSE of selected learnware: %.3f +/- %.3f, Average performance: %.3f +/- %.3f, Oracle performace: %.3f +/- %.3f" - % (np.mean(select_list[user]), np.std(select_list[user]), np.mean(avg_list[user]), np.std(avg_list[user]), np.mean(oracle_list[user]), np.std(oracle_list[user])) + f"single model num: {len(single_result)}, max_score: {single_result[0].score}, min_score: {single_result[-1].score}" ) + + pred_y = single_result[0].learnware.predict(test_x) + single_score_list.append(loss_func_rmse(pred_y, test_y)) + + rmse_list = [] + for learnware in all_learnwares: + hetero_learnware = FeatureAlignLearnware(learnware, **align_model_params) + hetero_learnware.align(user_rkme=user_stat_spec) + pred_y = hetero_learnware.predict(test_x) + rmse_list.append(loss_func_rmse(pred_y, test_y)) logger.info( - "Averaging Ensemble Reuse Performance: %.3f +/- %.3f" - % (np.mean(ensemble_score_list[user]), np.std(ensemble_score_list[user])) + f"Top1-score: {single_result[0].score}, learnware_id: {single_result[0].learnware.id}, rmse: {single_score_list[0]}" ) - for idx, n_labeled in enumerate(n_labeled_list): - n_labeled_score_list = [lst[idx] for lst in user_model_score_lists[user]] - logger.info( - "User Model with %d data: %.3f +/ %.3f" - % (n_labeled, np.mean(n_labeled_score_list), np.std(n_labeled_score_list)) - ) - - def test_hetero_labeled(self): - hetero_market = instantiate_learnware_market(market_id="heterogeneous", name="hetero") - logger.info("Total Items: %d" % len(hetero_market)) - - methods = ["user_model", "hetero_single_aug", "hetero_multiple_aug", "hetero_multiple_avg", "hetero_ensemble_pruning"] - methods_to_test = ["hetero_multiple_avg", "hetero_ensemble_pruning"] + + if len(multiple_result) > 0: + mixture_id = " ".join([learnware.id for learnware in multiple_result[0].learnwares]) + logger.info(f"mixture_score: {multiple_result[0].score}, mixture_learnware: {mixture_id}") + mixture_learnware_list = [] + for learnware in multiple_result[0].learnwares: + hetero_learnware = FeatureAlignLearnware(learnware, **align_model_params) + hetero_learnware.align(user_rkme=user_stat_spec) + mixture_learnware_list.append(hetero_learnware) + else: + hetero_learnware = FeatureAlignLearnware(single_result[0].learnware, **align_model_params) + hetero_learnware.align(user_rkme=user_stat_spec) + mixture_learnware_list = [hetero_learnware] + + # test reuse (job selector) + resue_baseline = JobSelectorReuser(learnware_list=mixture_learnware_list, herding_num=100) + reuse_predict = resue_baseline.predict(user_data=test_x) + reuse_score = loss_func_rmse(reuse_predict, test_y) + job_selector_score_list.append(reuse_score) + logger.info(f"mixture reuse rmse (job selector): {reuse_score}") + + # test reuse (ensemble) + reuse_ensemble = AveragingReuser(learnware_list=mixture_learnware_list, mode="mean") + ensemble_predict_y = reuse_ensemble.predict(user_data=test_x) + ensemble_score = loss_func_rmse(ensemble_predict_y, test_y) + ensemble_score_list[user].append(ensemble_score) + logger.info(f"mixture reuse rmse (ensemble): {ensemble_score}") + + learnware_rmse_list.append(rmse_list) + + single_list = np.array(learnware_rmse_list) + avg_score_list = [np.mean(lst, axis=0) for lst in single_list] + oracle_score_list = [np.min(lst, axis=0) for lst in single_list] + + logger.info( + "RMSE of selected learnware: %.3f +/- %.3f, Average performance: %.3f +/- %.3f, Oracle performace: %.3f +/- %.3f" + % ( + np.mean(single_score_list), + np.std(single_score_list), + np.mean(avg_score_list), + np.std(avg_score_list), + np.mean(oracle_score_list), + np.std(oracle_score_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)) + ) + + def labeled_hetero_table_example(self): + logger.info("Total Items: %d" % len(self.market)) + methods = ["user_model", "hetero_single_aug", "hetero_multiple_avg", "hetero_ensemble_pruning"] + methods_to_test = [] recorders = {method: Recorder() for method in methods + ["select_score", "oracle_score", "mean_score"]} - for user in default_users: - data_loader = DataLoader(user) - idx_list = data_loader.get_shop_ids() - for idx in idx_list: - _, _, test_x, test_y, feature_descriptions = data_loader.get_raw_data(idx) - train_subsets = data_loader.get_labeled_training_data( - idx, - size_list=n_labeled_list, - n_repeat_list=n_repeat_list - ) - user_stat_spec = specification.RKMETableSpecification() - user_stat_spec.generate_stat_spec(X=test_x) - - input_description = { - "Dimension": len(feature_descriptions), - "Description": {str(i): feature_descriptions[i] for i in range(len(feature_descriptions))} - } - user_semantic["Input"] = input_description - user_info = BaseUserInfo(semantic_spec=user_semantic, stat_info={"RKMETableSpecification": user_stat_spec}) - logger.info(f"Searching Market for user: {user}_{idx}") - - search_result = hetero_market.search_learnware(user_info, max_search_num=10) - single_result = search_result.get_single_results() - multiple_result = search_result.get_multiple_results() - - if len(multiple_result) > 0: - mixture_id = " ".join([learnware.id for learnware in multiple_result[0].learnwares]) - logger.info(f"Mixture score: {multiple_result[0].score}, Mixture learnware: {mixture_id}") - mixture_learnware_list = multiple_result[0].learnwares - else: - mixture_learnware_list = [single_result[0].learnware] - - logger.info(f"Hetero search result of user {user}_{idx}: mixture learnware num: {len(mixture_learnware_list)}") + user = self.benchmark.name + for idx in range(self.benchmark.user_num): + test_x, test_y = self.benchmark.get_test_data(user_ids=idx) + test_x, test_y = test_x.values, test_y.values + + train_x, train_y = self.benchmark.get_train_data(user_ids=idx) + train_x, train_y = train_x.values, train_y.values + train_subsets = self.get_train_subsets(idx, train_x, train_y) + + user_stat_spec = generate_stat_spec(type="table", X=test_x) + user_semantic = self.get_semantic_specifications(user_ids=idx) + user_info = BaseUserInfo( + semantic_spec=user_semantic, stat_info={user_stat_spec.type: user_stat_spec} + ) + logger.info(f"Searching Market for user: {user}_{idx}") - test_info = {"user": user, "idx": idx, "train_subsets": train_subsets, "test_x": test_x, "test_y": test_y} - common_config = {"user_rkme": user_stat_spec, "multiple_learnwares": mixture_learnware_list} - method_configs = { - "user_model": {"data_loader": data_loader}, - "hetero_single_aug": {"user_rkme": user_stat_spec, "learnwares": [single_result[0].learnware]}, - "hetero_multiple_aug": common_config, - "hetero_multiple_avg": common_config, - "hetero_ensemble_pruning": common_config - } + search_result = self.market.search_learnware(user_info, max_search_num=10) + single_result = search_result.get_single_results() + multiple_result = search_result.get_multiple_results() - for method_name in methods: - test_info["method_name"] = method_name - test_info["force"] = method_name in methods_to_test - test_info.update(method_configs[method_name]) - self.test_method(test_info, recorders, loss_func=loss_func_rmse) + if len(multiple_result) > 0: + mixture_id = " ".join([learnware.id for learnware in multiple_result[0].learnwares]) + logger.info(f"Mixture score: {multiple_result[0].score}, Mixture learnware: {mixture_id}") + mixture_learnware_list = multiple_result[0].learnwares + else: + mixture_learnware_list = [single_result[0].learnware] + logger.info(f"Hetero search result of user {user}_{idx}: mixture learnware num: {len(mixture_learnware_list)}") + + test_info = {"user": user, "idx": idx, "train_subsets": train_subsets, "test_x": test_x, "test_y": test_y} + common_config = {"user_rkme": user_stat_spec, "multiple_learnwares": mixture_learnware_list} + method_configs = { + "user_model": {"dataset": self.benchmark.name, "mode_type": "lgb"}, + "hetero_single_aug": {"user_rkme": user_stat_spec, "learnwares": [single_result[0].learnware]}, + "hetero_multiple_aug": common_config, + "hetero_multiple_avg": common_config, + "hetero_ensemble_pruning": common_config + } + + for method_name in methods: + logger.info(f"Testing method {method_name}") + test_info["method_name"] = method_name + test_info["force"] = method_name in methods_to_test + test_info.update(method_configs[method_name]) + self.test_method(test_info, recorders, loss_func=loss_func_rmse) + for method, recorder in recorders.items(): recorder.save(os.path.join(self.curves_result_path, f"{user}/{user}_{method}_performance.json")) methods_to_plot = ["user_model", "select_score", "hetero_multiple_avg", "hetero_ensemble_pruning"] - plot_performance_curves(user, {method: recorders[method] for method in methods_to_plot}, n_labeled_list=n_labeled_list) + plot_performance_curves(user, {method: recorders[method] for method in methods_to_plot}, task="Hetero", n_labeled_list=n_labeled_list) \ No newline at end of file From 05fa365e601f29144a9df882ef249f936486b432 Mon Sep 17 00:00:00 2001 From: liuht Date: Fri, 5 Jan 2024 18:10:48 +0800 Subject: [PATCH 06/32] [MNT] add cross task example --- examples/dataset_table_workflow/base.py | 56 ++++-- examples/dataset_table_workflow/config.py | 210 ++++++++++++++------ examples/dataset_table_workflow/hetero.py | 36 ++-- examples/dataset_table_workflow/homo.py | 6 +- examples/dataset_table_workflow/train.py | 2 +- examples/dataset_table_workflow/utils.py | 19 -- examples/dataset_table_workflow/workflow.py | 17 +- 7 files changed, 220 insertions(+), 126 deletions(-) diff --git a/examples/dataset_table_workflow/base.py b/examples/dataset_table_workflow/base.py index afe79b0..169ff9b 100644 --- a/examples/dataset_table_workflow/base.py +++ b/examples/dataset_table_workflow/base.py @@ -9,6 +9,7 @@ from tqdm import tqdm from learnware.client import LearnwareClient from learnware.logger import get_module_logger from learnware.market import instantiate_learnware_market +from learnware.reuse.utils import fill_data_with_mean from learnware.tests.benchmarks import LearnwareBenchmark from torch.multiprocessing import Process, Queue, set_start_method @@ -38,8 +39,12 @@ class TableWorkflow: @staticmethod def _limited_data(method, test_info, loss_func): + def subset_generator(): + for subset in test_info["train_subsets"]: + yield subset + all_scores = [] - for subset in test_info["train_subsets"]: + for subset in subset_generator(): subset_scores = [] for sample in subset: x_train, y_train = sample["x_train"], sample["y_train"] @@ -49,17 +54,18 @@ class TableWorkflow: return all_scores @staticmethod - def get_train_subsets(idx, train_x, train_y): + def get_train_subsets(n_labeled_list, n_repeat_list, idx, train_x, train_y): np.random.seed(idx) random.seed(idx) + train_x = fill_data_with_mean(train_x) train_subsets = [] for n_label, repeated in zip(n_labeled_list, n_repeat_list): train_subsets.append([]) if n_label > len(train_x): n_label = len(train_x) for _ in range(repeated): - x_train, y_train = zip(*random.sample(list(zip(train_x, train_y)), k=n_label)) - train_subsets[-1].append({"x_train": np.array(x_train), "y_train": np.array(list(y_train))}) + subset_idxs = np.random.choice(len(train_x), n_label, replace=False) + train_subsets[-1].append({"x_train": np.array(train_x[subset_idxs]), "y_train": np.array(train_y[subset_idxs])}) return train_subsets def _prepare_market(self, benchmark_config, name, rebuild): @@ -83,6 +89,16 @@ class TableWorkflow: time.sleep(1) continue + @staticmethod + def process_learnware_chunk(cuda_idx, method, test_info, loss_func, learnware_chunk, queue): + torch.cuda.set_device(cuda_idx) + for learnware in learnware_chunk: + learnware_index = test_info['learnwares'].index(learnware) + test_info['single_learnware'] = learnware + scores = TableWorkflow._limited_data(method, test_info, loss_func) + torch.cuda.empty_cache() + queue.put((learnware_index, scores)) + def test_method(self, test_info, recorders, loss_func=loss_func_rmse): method_name_full = test_info["method_name"] method_name = method_name_full if method_name_full == "user_model" else "_".join(method_name_full.split("_")[1:]) @@ -90,27 +106,20 @@ class TableWorkflow: user, idx = test_info["user"], test_info["idx"] recorder = recorders[method_name_full] - save_root_path = os.path.join(self.curves_result_path, user, f"{user}_{idx}") + save_root_path = os.path.join(self.curves_result_path, f"{user}_{test_info['n_labeled_list']}/{user}_{idx}") os.makedirs(save_root_path, exist_ok=True) save_path = os.path.join(save_root_path, f"{method_name}.json") if method_name_full == "hetero_single_aug": if test_info["force"] or recorder.should_test_method(user, idx, save_path): - # # single process testing - # all_scores = [] - # for learnware in test_info["learnwares"]: - # test_info["single_learnware"] = [learnware] - # all_scores.append(self._limited_data(test_methods[method_name_full], test_info, loss_func)) - # recorder.record(user, all_scores) - - # multi process testing + # * multi-process queue = Queue() processes = [] bar = tqdm(total=len(test_info["learnwares"]), desc=f"Test {method_name}", unit="learnware") learnware_chunks = [test_info["learnwares"][i:len(test_info["learnwares"]):len(self.cuda_idx)] for i in self.cuda_idx] for cuda_idx, learnware_chunk in zip(self.cuda_idx, learnware_chunks): - p = Process(target=self.process_learnware_chunk, args=(cuda_idx, method, test_info, loss_func, learnware_chunk, queue)) + p = Process(target=TableWorkflow.process_learnware_chunk, args=(cuda_idx, method, test_info, loss_func, learnware_chunk, queue)) processes.append(p) p.start() @@ -131,14 +140,23 @@ class TableWorkflow: all_results.sort(key=lambda x: x[0]) all_scores = [result[1] for result in all_results] recorder.record(user, all_scores) - # process_single_aug(user, idx, all_scores, recorders, save_root_path) + + # * single-process + # bar = tqdm(total=len(test_info["learnwares"]), desc=f"Test {method_name}") + # for learnware in test_info['learnwares']: + # test_info['single_learnware'] = learnware + # scores = self._limited_data(test_methods[method_name_full], test_info, loss_func) + # recorder.record(user, idx, scores) + # bar.update(1) + + process_single_aug(user, idx, all_scores, recorders, save_root_path) recorder.save(save_path) - # else: - process_single_aug(user, idx, recorder.data[user][idx], recorders, save_root_path) + else: + process_single_aug(user, idx, recorder.data[user][idx], recorders, save_root_path) else: if test_info["force"] or recorder.should_test_method(user, idx, save_path): - scores = self._limited_data(test_methods[method_name_full], test_info, loss_func) + scores = self._limited_data(method, test_info, loss_func) recorder.record(user, scores) recorder.save(save_path) - + logger.info(f"Method {method_name} on {user}_{idx} finished") \ No newline at end of file diff --git a/examples/dataset_table_workflow/config.py b/examples/dataset_table_workflow/config.py index 14b60ed..fd931c4 100644 --- a/examples/dataset_table_workflow/config.py +++ b/examples/dataset_table_workflow/config.py @@ -1,8 +1,20 @@ from learnware.tests.benchmarks import BenchmarkConfig -n_labeled_list = [100, 200, 500, 1000, 2000, 4000, 6000, 8000, 10000] -n_repeat_list = [10, 10, 10, 3, 3, 3, 3, 3, 3] +homo_n_labeled_list = [100, 200, 500, 1000, 2000, 4000, 6000, 8000, 10000] +homo_n_repeat_list = [10, 10, 10, 3, 3, 3, 3, 3, 3] +hetero_n_labeled_list = [10, 30, 50, 75, 100, 200] +hetero_n_repeat_list = [10, 10, 10, 10, 10, 10] + + +user_semantic = { + "Data": {"Values": ["Table"], "Type": "Class"}, + "Task": {"Values": ["Regression"], "Type": "Class"}, + "Library": {"Values": ["Others"], "Type": "Class"}, + "Scenario": {"Values": ["Business"], "Type": "Tag"}, + "Description": {"Values": "", "Type": "String"}, + "Name": {"Values": "", "Type": "String"}, +} styles = { 'user_model': {"color": "navy", "marker": "o", "linestyle": "-"}, @@ -35,16 +47,16 @@ align_model_params = { } market_mapping_params = { - "lr": 1e-4, # [5e-5, 1e-4, 2e-4, 5e-4], + "lr": 1e-4, "num_epoch": 50, - "batch_size": 64, # [64, 128, 256, 512, 1024], - "num_partition": 2, # [2, 3, 4], # num of column partitions for pos/neg sampling - "overlap_ratio": 0.7, # [0.1, 0.3, 0.5, 0.7], # specify the overlap ratio of column partitions during the CL - "hidden_dim": 256, # [64, 128, 256, 512, 768, 1024], # the dimension of hidden embeddings - "num_layer": 6, # [4, 6, 8, 10, 12, 14, 16, 20], # the number of transformer layers used in the encoder - "num_attention_head": 8, # [4, 8, 16], # the numebr of heads of multihead self-attention layer in the transformers, should be divisible by hidden_dim - "hidden_dropout_prob": 0.5, # [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6], # the dropout ratio in the transformer encoder - "ffn_dim": 512, # [128, 256, 512, 768, 1024], # the dimension of feed-forward layer in the transformer layer + "batch_size": 64, + "num_partition": 2, # num of column partitions for pos/neg sampling + "overlap_ratio": 0.7, # specify the overlap ratio of column partitions during the CL + "hidden_dim": 256, # the dimension of hidden embeddings + "num_layer": 6, # the number of transformer layers used in the encoder + "num_attention_head": 8, # the numebr of heads of multihead self-attention layer in the transformers, should be divisible by hidden_dim + "hidden_dropout_prob": 0.5, # the dropout ratio in the transformer encoder + "ffn_dim": 512, # the dimension of feed-forward layer in the transformer layer "activation": "leakyrelu", } @@ -65,6 +77,24 @@ user_model_params = { "MAX_ROUNDS": 500, "early_stopping_rounds": 50, } + }, + "M5": { + "lgb": { + "params": { + "boosting_type": "gbdt", + "objective": "rmse", + "metric": "rmse", + "learning_rate": 0.015, + "num_leaves": 300, + "max_depth": 500, + "n_estimators": 100000, + "boost_from_average": False, + "num_threads": 32, + "verbose": -1 + }, + "MAX_ROUNDS": 1000, + "early_stopping_rounds": 1000 + } } } @@ -72,62 +102,110 @@ homo_table_benchmark_config = BenchmarkConfig( name="Corporacion", user_num=54, learnware_ids=[ - "00000912", - "00000911", - "00000910", - "00000909", - "00000908", - "00000907", - "00000906", - "00000905", - "00000904", - "00000903", - "00000902", - "00000901", - "00000900", - "00000899", - "00000898", - "00000897", - "00000896", - "00000895", - "00000894", - "00000893", - "00000892", - "00000891", - "00000890", - "00000889", - "00000888", - "00000887", - "00000886", - "00000885", - "00000884", - "00000883", - "00000882", - "00000881", - "00000880", - "00000879", - "00000878", - "00000877", - "00000876", - "00000875", - "00000874", - "00000873", - "00000872", - "00000871", - "00000870", - "00000869", - "00000868", - "00000867", - "00000866", - "00000865", - "00000864", - "00000863", - "00000862", - "00000861", - "00000860", - "00000859" + "00000912", "00000911", "00000910", "00000909", + "00000908", "00000907", "00000906", "00000905", + "00000904", "00000903", "00000902", "00000901", + "00000900", "00000899", "00000898", "00000897", + "00000896", "00000895", "00000894", "00000893", + "00000892", "00000891", "00000890", "00000889", + "00000888", "00000887", "00000886", "00000885", + "00000884", "00000883", "00000882", "00000881", + "00000880", "00000879", "00000878", "00000877", + "00000876", "00000875", "00000874", "00000873", + "00000872", "00000871", "00000870", "00000869", + "00000868", "00000867", "00000866", "00000865", + "00000864", "00000863", "00000862", "00000861", + "00000860", "00000859" ], test_data_path="Corporacion/test_data.zip", train_data_path="Corporacion/train_data.zip", - extra_info_path="Corporacion/extra_info.zip", + extra_info_path="Corporacion/extra_info.zip" +) + +hetero_cross_feat_eng_benchmark_config = BenchmarkConfig( + name="PFS", + user_num=41, + learnware_ids = [ + + ], + test_data_path=None, + train_data_path=None, + extra_info_path=None +) + +hetero_cross_task_benchmark_config = BenchmarkConfig( + name="M5", + user_num=9, + learnware_ids = [ + "00000394", "00000393", "00000392", "00000391", + "00000390", "00000389", "00000388", "00000387", + "00000386", "00000385", "00000384", "00000383", + "00000382", "00000381", "00000380", "00000379", + "00000378", "00000377", "00000376", "00000375", + "00000374", "00000373", "00000372", "00000371", + "00000370", "00000369", "00000368", "00000367", + "00000366", "00000365", "00000364", "00000363", + "00000362", "00000361", "00000360", "00000359", + "00000358", "00000357", "00000356", "00000355", + "00000354", "00000353", "00000352", "00000351", + "00000350", "00000349", "00000348", "00000347", + "00000346", "00000345", "00000344", "00000343", + "00000342", "00000444", "00000443", "00000442", + "00000441", "00000440", "00000439", "00000438", + "00000437", "00000436", "00000435", "00000434", + "00000433", "00000432", "00000431", "00000430", + "00000429", "00000428", "00000427", "00000426", + "00000425", "00000424", "00000423", "00000422", + "00000421", "00000420", "00000419", "00000418", + "00000417", "00000416", "00000415", "00000414", + "00000413", "00000412", "00000411", "00000410", + "00000409", "00000408", "00000407", "00000406", + "00000405", "00000404", "00000403", "00000402", + "00000401", "00000400", "00000399", "00000398", + "00000397", "00000396", "00000395", "00000783", + "00000782", "00000781", "00000780", "00000779", + "00000778", "00000777", "00000776", "00000775", + "00000774", "00000773", "00000772", "00000771", + "00000770", "00000769", "00000768", "00000767", + "00000766", "00000765", "00000764", "00000763", + "00000762", "00000761", "00000760", "00000759", + "00000758", "00000757", "00000756", "00000755", + "00000754", "00000753", "00000752", "00000751", + "00000750", "00000749", "00000748", "00000747", + "00000746", "00000745", "00000744", "00000743", + "00000742", "00000741", "00000740", "00000739", + "00000738", "00000737", "00000736", "00000735", + "00000734", "00000733", "00000732", "00000731", + "00000730", "00000839", "00000838", "00000837", + "00000836", "00000835", "00000834", "00000833", + "00000832", "00000831", "00000830", "00000829", + "00000828", "00000827", "00000826", "00000825", + "00000824", "00000823", "00000822", "00000821", + "00000820", "00000819", "00000818", "00000817", + "00000816", "00000815", "00000814", "00000813", + "00000812", "00000811", "00000810", "00000809", + "00000808", "00000807", "00000806", "00000805", + "00000804", "00000803", "00000802", "00000801", + "00000800", "00000799", "00000798", "00000797", + "00000796", "00000795", "00000794", "00000793", + "00000792", "00000791", "00000790", "00000789", + "00000788", "00000787", "00000786", "00000912", + "00000911", "00000910", "00000909", "00000908", + "00000907", "00000906", "00000905", "00000904", + "00000903", "00000902", "00000901", "00000900", + "00000899", "00000898", "00000897", "00000896", + "00000895", "00000894", "00000893", "00000892", + "00000891", "00000890", "00000889", "00000888", + "00000887", "00000886", "00000885", "00000884", + "00000883", "00000882", "00000881", "00000880", + "00000879", "00000878", "00000877", "00000876", + "00000875", "00000874", "00000873", "00000872", + "00000871", "00000870", "00000869", "00000868", + "00000867", "00000866", "00000865", "00000864", + "00000863", "00000862", "00000861", "00000860", + "00000859" + ], + test_data_path="M5/test_data.zip", + train_data_path="M5/train_data.zip", + extra_info_path="M5/extra_info.zip" ) \ No newline at end of file diff --git a/examples/dataset_table_workflow/hetero.py b/examples/dataset_table_workflow/hetero.py index 246fd06..ca06890 100644 --- a/examples/dataset_table_workflow/hetero.py +++ b/examples/dataset_table_workflow/hetero.py @@ -1,19 +1,17 @@ import os import warnings -from collections import defaultdict -from functools import partial warnings.filterwarnings("ignore") import json import numpy as np from learnware.logger import get_module_logger from learnware.specification import generate_stat_spec -from learnware.market import instantiate_learnware_market, BaseUserInfo +from learnware.market import BaseUserInfo from learnware.reuse import AveragingReuser, JobSelectorReuser, FeatureAlignLearnware from methods import * from base import TableWorkflow -from config import align_model_params +from config import align_model_params, user_semantic, hetero_n_labeled_list, hetero_n_repeat_list from utils import Recorder, plot_performance_curves logger = get_module_logger("hetero_test", level="INFO") @@ -128,19 +126,27 @@ class HeterogeneousDatasetWorkflow(TableWorkflow): test_x, test_y = test_x.values, test_y.values train_x, train_y = self.benchmark.get_train_data(user_ids=idx) - train_x, train_y = train_x.values, train_y.values - train_subsets = self.get_train_subsets(idx, train_x, train_y) + train_x, train_y, feature_descriptions = train_x.values, train_y.values, train_x.columns + train_subsets = self.get_train_subsets(hetero_n_labeled_list, hetero_n_repeat_list, idx, train_x, train_y) user_stat_spec = generate_stat_spec(type="table", X=test_x) - user_semantic = self.get_semantic_specifications(user_ids=idx) + input_description = { + "Dimension": len(feature_descriptions), + "Description": {str(i): feature_descriptions[i] for i in range(len(feature_descriptions))} + } + user_semantic["Input"] = input_description user_info = BaseUserInfo( semantic_spec=user_semantic, stat_info={user_stat_spec.type: user_stat_spec} ) logger.info(f"Searching Market for user: {user}_{idx}") - search_result = self.market.search_learnware(user_info, max_search_num=10) + search_result = self.market.search_learnware(user_info) single_result = search_result.get_single_results() multiple_result = search_result.get_multiple_results() + + rank_map = {item.learnware.id: index for index, item in enumerate(single_result)} + all_learnwares = self.market.get_learnwares() + all_learnwares.sort(key=lambda learnware: rank_map.get(learnware.id, float('inf'))) if len(multiple_result) > 0: mixture_id = " ".join([learnware.id for learnware in multiple_result[0].learnwares]) @@ -151,11 +157,11 @@ class HeterogeneousDatasetWorkflow(TableWorkflow): logger.info(f"Hetero search result of user {user}_{idx}: mixture learnware num: {len(mixture_learnware_list)}") - test_info = {"user": user, "idx": idx, "train_subsets": train_subsets, "test_x": test_x, "test_y": test_y} + test_info = {"user": user, "idx": idx, "train_subsets": train_subsets, "test_x": test_x, "test_y": test_y, "n_labeled_list": hetero_n_labeled_list} common_config = {"user_rkme": user_stat_spec, "multiple_learnwares": mixture_learnware_list} method_configs = { - "user_model": {"dataset": self.benchmark.name, "mode_type": "lgb"}, - "hetero_single_aug": {"user_rkme": user_stat_spec, "learnwares": [single_result[0].learnware]}, + "user_model": {"dataset": self.benchmark.name, "model_type": "lgb"}, + "hetero_single_aug": {"user_rkme": user_stat_spec, "learnwares": all_learnwares}, "hetero_multiple_aug": common_config, "hetero_multiple_avg": common_config, "hetero_ensemble_pruning": common_config @@ -168,9 +174,9 @@ class HeterogeneousDatasetWorkflow(TableWorkflow): test_info.update(method_configs[method_name]) self.test_method(test_info, recorders, loss_func=loss_func_rmse) - for method, recorder in recorders.items(): - recorder.save(os.path.join(self.curves_result_path, f"{user}/{user}_{method}_performance.json")) + for method, recorder in recorders.items(): + recorder.save(os.path.join(self.curves_result_path, f"{user}/{user}_{method}_performance.json")) - methods_to_plot = ["user_model", "select_score", "hetero_multiple_avg", "hetero_ensemble_pruning"] - plot_performance_curves(user, {method: recorders[method] for method in methods_to_plot}, task="Hetero", n_labeled_list=n_labeled_list) + methods_to_plot = ["user_model", "select_score", "hetero_multiple_avg", "hetero_ensemble_pruning"] + plot_performance_curves(user, {method: recorders[method] for method in methods_to_plot}, task="Hetero", n_labeled_list=n_labeled_list) \ No newline at end of file diff --git a/examples/dataset_table_workflow/homo.py b/examples/dataset_table_workflow/homo.py index 03e45a9..282b9a2 100644 --- a/examples/dataset_table_workflow/homo.py +++ b/examples/dataset_table_workflow/homo.py @@ -10,7 +10,7 @@ from learnware.reuse import AveragingReuser, JobSelectorReuser, EnsemblePruningR from methods import * from base import TableWorkflow -from config import n_labeled_list +from config import homo_n_labeled_list, homo_n_repeat_list from utils import Recorder, plot_performance_curves logger = get_module_logger("homo_table", level="INFO") @@ -127,7 +127,7 @@ class CorporacionDatasetWorkflow(TableWorkflow): train_x, train_y = self.benchmark.get_train_data(user_ids=idx) train_x, train_y = train_x.values, train_y.values - train_subsets = self.get_train_subsets(idx, train_x, train_y) + train_subsets = self.get_train_subsets(homo_n_labeled_list, homo_n_repeat_list, idx, train_x, train_y) user_stat_spec = generate_stat_spec(type="table", X=test_x) user_info = BaseUserInfo( @@ -172,4 +172,4 @@ class CorporacionDatasetWorkflow(TableWorkflow): recorder.save(os.path.join(self.curves_result_path, f"{user}_{method}_performance.json")) methods_to_plot = ["user_model", "homo_single_aug", "homo_ensemble_pruning"] - plot_performance_curves(user, {method: recorders[method] for method in methods_to_plot}, task="Homo", n_labeled_list=n_labeled_list) \ No newline at end of file + plot_performance_curves(user, {method: recorders[method] for method in methods_to_plot}, task="Homo", n_labeled_list=homo_n_labeled_list) \ No newline at end of file diff --git a/examples/dataset_table_workflow/train.py b/examples/dataset_table_workflow/train.py index 7ac2b3a..b118d16 100644 --- a/examples/dataset_table_workflow/train.py +++ b/examples/dataset_table_workflow/train.py @@ -26,7 +26,7 @@ def train_lgb(X_train, y_train, X_val, y_val, dataset): params, dtrain, num_boost_round=MAX_ROUNDS, - valid_sets=[dtrain, dval], + valid_sets=[dtrain, dval] if dataset == "Corporacion" else [dval], callbacks=[early_stopping(model_param["early_stopping_rounds"], verbose=False)] ) val_pred.append(bst.predict(X_val, num_iteration=bst.best_iteration or MAX_ROUNDS)) diff --git a/examples/dataset_table_workflow/utils.py b/examples/dataset_table_workflow/utils.py index fd31064..4dc6471 100644 --- a/examples/dataset_table_workflow/utils.py +++ b/examples/dataset_table_workflow/utils.py @@ -58,25 +58,6 @@ def process_single_aug(user, idx, scores, recorders, root_path): logger.error(f"Error in process_single_aug for user {user}, idx {idx}: {error_message}") -def analyze_performance(user, recorders): - oracle_score_list = recorders["hetero_oracle_score"].get_performance_data(user) - select_score_list = recorders["hetero_select_score"].get_performance_data(user) - multi_avg_score_list = recorders["hetero_multiple_avg"].get_performance_data(user) - mean_differences = {} - - for user_id in range(len(oracle_score_list)): - select_scores = select_score_list[user_id] - oracle_scores = oracle_score_list[user_id] - mean_difference = np.mean(select_scores) - np.mean(oracle_scores) - mean_differences[user_id] = mean_difference - - sorted_user_ids = sorted(mean_differences, key=mean_differences.get, reverse=True) - - for user_id in sorted_user_ids: - single_multi_diff = np.mean(select_score_list[user_id]) - np.mean(multi_avg_score_list[user_id]) - logger.info(f"{user}, {user_id}, {mean_differences[user_id]}, {single_multi_diff}") - - def plot_performance_curves(user, recorders, task, n_labeled_list): plt.figure(figsize=(10, 6)) plt.xticks(range(len(n_labeled_list)), n_labeled_list) diff --git a/examples/dataset_table_workflow/workflow.py b/examples/dataset_table_workflow/workflow.py index 3f0a319..c31420d 100644 --- a/examples/dataset_table_workflow/workflow.py +++ b/examples/dataset_table_workflow/workflow.py @@ -2,7 +2,8 @@ import fire from learnware.logger import get_module_logger from homo import CorporacionDatasetWorkflow -from config import homo_table_benchmark_config +from hetero import HeterogeneousDatasetWorkflow +from config import homo_table_benchmark_config, hetero_cross_feat_eng_benchmark_config, hetero_cross_task_benchmark_config logger = get_module_logger("base_table", level="INFO") @@ -25,10 +26,20 @@ class TableDatasetWorkflow: workflow.labeled_homo_table_example() def cross_feat_eng_hetero_table_example(self): - pass + workflow = HeterogeneousDatasetWorkflow( + benchmark_config=hetero_cross_feat_eng_benchmark_config, + name="hetero", + rebuild=False + ) + workflow.unlabeled_hetero_table_example() def cross_task_hetero_table_example(self): - pass + workflow = HeterogeneousDatasetWorkflow( + benchmark_config=hetero_cross_task_benchmark_config, + name="hetero", + rebuild=False + ) + workflow.labeled_hetero_table_example() if __name__ == "__main__": From f4115cf102b2aa62e9007af698fa6c496033161a Mon Sep 17 00:00:00 2001 From: liuht Date: Fri, 5 Jan 2024 20:58:53 +0800 Subject: [PATCH 07/32] [MNT] add cross feature engineering example --- examples/dataset_table_workflow/base.py | 6 +- examples/dataset_table_workflow/config.py | 97 +++++++++++++++++++-- examples/dataset_table_workflow/hetero.py | 2 +- examples/dataset_table_workflow/workflow.py | 2 +- 4 files changed, 96 insertions(+), 11 deletions(-) diff --git a/examples/dataset_table_workflow/base.py b/examples/dataset_table_workflow/base.py index 169ff9b..5a484bb 100644 --- a/examples/dataset_table_workflow/base.py +++ b/examples/dataset_table_workflow/base.py @@ -149,10 +149,10 @@ class TableWorkflow: # recorder.record(user, idx, scores) # bar.update(1) - process_single_aug(user, idx, all_scores, recorders, save_root_path) + # process_single_aug(user, idx, all_scores, recorders, save_root_path) recorder.save(save_path) - else: - process_single_aug(user, idx, recorder.data[user][idx], recorders, save_root_path) + #else: + process_single_aug(user, idx, recorder.data[user][idx], recorders, save_root_path) else: if test_info["force"] or recorder.should_test_method(user, idx, save_path): scores = self._limited_data(method, test_info, loss_func) diff --git a/examples/dataset_table_workflow/config.py b/examples/dataset_table_workflow/config.py index fd931c4..33c060e 100644 --- a/examples/dataset_table_workflow/config.py +++ b/examples/dataset_table_workflow/config.py @@ -29,8 +29,8 @@ styles = { labels = { 'user_model': "User Model", - 'single_aug': "Single Learnware Reuse (Select)", - "select_score": "Single Learnware Reuse (Select)", + 'single_aug': "Single Learnware Reuse (FeatAug)", + "select_score": "Single Learnware Reuse (FeatAug)", 'multiple_aug': "Multiple Learnware Reuse (FeatAug)", 'ensemble_pruning': "Multiple Learnware Reuse (EnsemblePrune)", 'multiple_avg': "Multiple Learnware Reuse (Averaging)" @@ -95,6 +95,25 @@ user_model_params = { "MAX_ROUNDS": 1000, "early_stopping_rounds": 1000 } + }, + "PFS": { + "lgb": { + "params": { + "boosting_type": "gbdt", + "num_leaves": 2**7 - 1, + "learning_rate": 0.01, + "objective": "rmse", + "metric": "rmse", + "feature_fraction": 0.75, + "bagging_fraction": 0.75, + "bagging_freq": 5, + "seed": 1, + "verbose": -100, + "n_estimators": 100000, + }, + "MAX_ROUNDS": 1000, + "early_stopping_rounds": 1000 + } } } @@ -126,11 +145,77 @@ hetero_cross_feat_eng_benchmark_config = BenchmarkConfig( name="PFS", user_num=41, learnware_ids = [ - + "00000394", "00000393", "00000392", "00000391", + "00000390", "00000389", "00000388", "00000387", + "00000386", "00000385", "00000384", "00000383", + "00000382", "00000381", "00000380", "00000379", + "00000378", "00000377", "00000376", "00000375", + "00000374", "00000373", "00000372", "00000371", + "00000370", "00000369", "00000368", "00000367", + "00000366", "00000365", "00000364", "00000363", + "00000362", "00000361", "00000360", "00000359", + "00000358", "00000357", "00000356", "00000355", + "00000354", "00000353", "00000352", "00000351", + "00000350", "00000349", "00000348", "00000347", + "00000346", "00000345", "00000344", "00000343", + "00000342", "00000444", "00000443", "00000442", + "00000441", "00000440", "00000439", "00000438", + "00000437", "00000436", "00000435", "00000434", + "00000433", "00000432", "00000431", "00000430", + "00000429", "00000428", "00000427", "00000426", + "00000425", "00000424", "00000423", "00000422", + "00000421", "00000420", "00000419", "00000418", + "00000417", "00000416", "00000415", "00000414", + "00000413", "00000412", "00000411", "00000410", + "00000409", "00000408", "00000407", "00000406", + "00000405", "00000404", "00000403", "00000402", + "00000401", "00000400", "00000399", "00000398", + "00000397", "00000396", "00000395", "00000783", + "00000782", "00000781", "00000780", "00000779", + "00000778", "00000777", "00000776", "00000775", + "00000774", "00000773", "00000772", "00000771", + "00000770", "00000769", "00000768", "00000767", + "00000766", "00000765", "00000764", "00000763", + "00000762", "00000761", "00000760", "00000759", + "00000758", "00000757", "00000756", "00000755", + "00000754", "00000753", "00000752", "00000751", + "00000750", "00000749", "00000748", "00000747", + "00000746", "00000745", "00000744", "00000743", + "00000742", "00000741", "00000740", "00000739", + "00000738", "00000737", "00000736", "00000735", + "00000734", "00000733", "00000732", "00000731", + "00000730", "00000839", "00000838", "00000837", + "00000836", "00000835", "00000834", "00000833", + "00000832", "00000831", "00000830", "00000829", + "00000828", "00000827", "00000826", "00000825", + "00000824", "00000823", "00000822", "00000821", + "00000820", "00000819", "00000818", "00000817", + "00000816", "00000815", "00000814", "00000813", + "00000812", "00000811", "00000810", "00000809", + "00000808", "00000807", "00000806", "00000805", + "00000804", "00000803", "00000802", "00000801", + "00000800", "00000799", "00000798", "00000797", + "00000796", "00000795", "00000794", "00000793", + "00000792", "00000791", "00000790", "00000789", + "00000788", "00000787", "00000786", "00000912", + "00000911", "00000910", "00000909", "00000908", + "00000907", "00000906", "00000905", "00000904", + "00000903", "00000902", "00000901", "00000900", + "00000899", "00000898", "00000897", "00000896", + "00000895", "00000894", "00000893", "00000892", + "00000891", "00000890", "00000889", "00000888", + "00000887", "00000886", "00000885", "00000884", + "00000883", "00000882", "00000881", "00000880", + "00000879", "00000878", "00000877", "00000876", + "00000875", "00000874", "00000873", "00000872", + "00000871", "00000870", "00000869", "00000868", + "00000867", "00000866", "00000865", "00000864", + "00000863", "00000862", "00000861", "00000860", + "00000859" ], - test_data_path=None, - train_data_path=None, - extra_info_path=None + test_data_path="PFS/test_data.zip", + train_data_path="PFS/train_data.zip", + extra_info_path="PFS/extra_info.zip" ) hetero_cross_task_benchmark_config = BenchmarkConfig( diff --git a/examples/dataset_table_workflow/hetero.py b/examples/dataset_table_workflow/hetero.py index ca06890..f365971 100644 --- a/examples/dataset_table_workflow/hetero.py +++ b/examples/dataset_table_workflow/hetero.py @@ -158,7 +158,7 @@ class HeterogeneousDatasetWorkflow(TableWorkflow): logger.info(f"Hetero search result of user {user}_{idx}: mixture learnware num: {len(mixture_learnware_list)}") test_info = {"user": user, "idx": idx, "train_subsets": train_subsets, "test_x": test_x, "test_y": test_y, "n_labeled_list": hetero_n_labeled_list} - common_config = {"user_rkme": user_stat_spec, "multiple_learnwares": mixture_learnware_list} + common_config = {"user_rkme": user_stat_spec, "learnwares": mixture_learnware_list} method_configs = { "user_model": {"dataset": self.benchmark.name, "model_type": "lgb"}, "hetero_single_aug": {"user_rkme": user_stat_spec, "learnwares": all_learnwares}, diff --git a/examples/dataset_table_workflow/workflow.py b/examples/dataset_table_workflow/workflow.py index c31420d..902e9fd 100644 --- a/examples/dataset_table_workflow/workflow.py +++ b/examples/dataset_table_workflow/workflow.py @@ -29,7 +29,7 @@ class TableDatasetWorkflow: workflow = HeterogeneousDatasetWorkflow( benchmark_config=hetero_cross_feat_eng_benchmark_config, name="hetero", - rebuild=False + rebuild=True ) workflow.unlabeled_hetero_table_example() From 999833e421914c71dffa4786fb6a64a574ff4a27 Mon Sep 17 00:00:00 2001 From: liuht Date: Fri, 5 Jan 2024 21:22:57 +0800 Subject: [PATCH 08/32] [MNT] modify details --- examples/dataset_table_workflow/base.py | 12 ++-- examples/dataset_table_workflow/train.py | 4 -- examples/dataset_table_workflow/utils.py | 86 ++++++++++++++---------- 3 files changed, 56 insertions(+), 46 deletions(-) diff --git a/examples/dataset_table_workflow/base.py b/examples/dataset_table_workflow/base.py index 5a484bb..1a1229e 100644 --- a/examples/dataset_table_workflow/base.py +++ b/examples/dataset_table_workflow/base.py @@ -106,7 +106,7 @@ class TableWorkflow: user, idx = test_info["user"], test_info["idx"] recorder = recorders[method_name_full] - save_root_path = os.path.join(self.curves_result_path, f"{user}_{test_info['n_labeled_list']}/{user}_{idx}") + save_root_path = os.path.join(self.curves_result_path, f"{user}/{user}_{idx}") os.makedirs(save_root_path, exist_ok=True) save_path = os.path.join(save_root_path, f"{method_name}.json") @@ -140,6 +140,9 @@ class TableWorkflow: all_results.sort(key=lambda x: x[0]) all_scores = [result[1] for result in all_results] recorder.record(user, all_scores) + recorder.save(save_path) + + process_single_aug(user, idx, recorder.data[user][idx], recorders, save_root_path) # * single-process # bar = tqdm(total=len(test_info["learnwares"]), desc=f"Test {method_name}") @@ -147,12 +150,7 @@ class TableWorkflow: # test_info['single_learnware'] = learnware # scores = self._limited_data(test_methods[method_name_full], test_info, loss_func) # recorder.record(user, idx, scores) - # bar.update(1) - - # process_single_aug(user, idx, all_scores, recorders, save_root_path) - recorder.save(save_path) - #else: - process_single_aug(user, idx, recorder.data[user][idx], recorders, save_root_path) + # bar.update(1) else: if test_info["force"] or recorder.should_test_method(user, idx, save_path): scores = self._limited_data(method, test_info, loss_func) diff --git a/examples/dataset_table_workflow/train.py b/examples/dataset_table_workflow/train.py index b118d16..c2ad170 100644 --- a/examples/dataset_table_workflow/train.py +++ b/examples/dataset_table_workflow/train.py @@ -18,8 +18,6 @@ def train_lgb(X_train, y_train, X_val, y_val, dataset): val_pred = [] cate_vars = [] - logger.info(f"{np.shape(X_train)}, {np.shape(y_train)}, {np.shape(X_val)}, {np.shape(y_val)}") - dtrain = lgb.Dataset(X_train, label=y_train, categorical_feature=cate_vars) dval = lgb.Dataset(X_val, label=y_val, reference=dtrain, categorical_feature=cate_vars) bst = lgb.train( @@ -30,8 +28,6 @@ def train_lgb(X_train, y_train, X_val, y_val, dataset): callbacks=[early_stopping(model_param["early_stopping_rounds"], verbose=False)] ) val_pred.append(bst.predict(X_val, num_iteration=bst.best_iteration or MAX_ROUNDS)) - logger.info(f"Validation mse:{mean_squared_error(y_val, np.array(val_pred).transpose())}") - return bst diff --git a/examples/dataset_table_workflow/utils.py b/examples/dataset_table_workflow/utils.py index 4dc6471..b14358c 100644 --- a/examples/dataset_table_workflow/utils.py +++ b/examples/dataset_table_workflow/utils.py @@ -27,7 +27,7 @@ class Recorder: def save(self, path): with open(path, "w") as f: json.dump(self.data, f, indent=4, default=list) - + def load(self, path): with open(path, "r") as f: self.data = json.load(f, object_hook=lambda x: defaultdict(list, x)) @@ -42,57 +42,73 @@ class Recorder: def process_single_aug(user, idx, scores, recorders, root_path): try: scores_array = np.array(scores) + n_labeled = len(scores_array[0]) + while scores_array.ndim < 3: scores_array = scores_array[np.newaxis, :] - select_scores = scores_array[:, 0, :].tolist() - mean_scores = np.mean(scores_array, axis=1).tolist() - oracle_scores = np.min(scores_array, axis=1).tolist() + + select_scores, mean_scores, oracle_scores = [], [], [] + for i in range(n_labeled): + sub_scores_array = np.vstack([lst[i] for lst in scores_array]) + sub_scores_select = np.squeeze(sub_scores_array[0]) + sub_scores_mean = np.squeeze(np.mean(sub_scores_array, axis=0)) + sub_scores_min = np.squeeze(np.min(sub_scores_array, axis=0)) + + select_scores.append(sub_scores_select.tolist()) + mean_scores.append(sub_scores_mean.tolist()) + oracle_scores.append(sub_scores_min.tolist()) for method_name, scores in zip(["select_score", "mean_score", "oracle_score"], [select_scores, mean_scores, oracle_scores]): recorders[method_name].record(user, scores) - save_path = os.path.join(root_path, f"{method_name}_performance.json") + save_path = os.path.join(root_path, f"{method_name}.json") recorders[method_name].save(save_path) - except Exception as e: + + except Exception: error_message = traceback.format_exc() logger.error(f"Error in process_single_aug for user {user}, idx {idx}: {error_message}") -def plot_performance_curves(user, recorders, task, n_labeled_list): +def plot_performance_curves(path, users, idxs, recorders, task, n_labeled_list): plt.figure(figsize=(10, 6)) plt.xticks(range(len(n_labeled_list)), n_labeled_list) - for method, recorder in recorders.items(): - if method == "hetero_single_aug": - continue - - scores_array = recorder.get_performance_data(user) - if scores_array: - mean_curve, std_curve = [], [] - for i in range(len(n_labeled_list)): - sub_scores_array = np.vstack([lst[i] for lst in scores_array]) - sub_scores_mean = np.squeeze(np.mean(sub_scores_array, axis=0)) - mean_curve.append(np.mean(sub_scores_mean)) - std_curve.append(np.std(sub_scores_mean)) - - mean_curve = np.array(mean_curve) - std_curve = np.array(std_curve) - - method_plot = '_'.join(method.split('_')[1:]) if method not in ['user_model', 'oracle_score', 'select_score', 'mean_score'] else method - style = styles.get(method_plot, {"color": "black", "linestyle": "-"}) - plt.plot(mean_curve, label=labels.get(method_plot), **style) - - plt.fill_between( - range(len(mean_curve)), - mean_curve - std_curve, - mean_curve + std_curve, - color=style["color"], - alpha=0.2 - ) + all_scores_array = [] + for user in users: + if method == "hetero_single_aug": + continue + + data_path = os.path.join(path, f"{user}_{n_labeled_list}/{user}_{method}_performance.json") + recorder.load(data_path) + scores_array = recorder.get_performance_data(user) + if scores_array: + all_scores_array.extend([scores_array[idx] for idx in idxs]) + + mean_curve, std_curve = [], [] + for i in range(len(n_labeled_list)): + sub_scores_array = np.vstack([lst[i] for lst in all_scores_array]) + sub_scores_mean = np.squeeze(np.mean(sub_scores_array, axis=0)) + mean_curve.append(np.mean(sub_scores_mean)) + std_curve.append(np.std(sub_scores_mean)) + + mean_curve = np.array(mean_curve) + std_curve = np.array(std_curve) + + method_plot = '_'.join(method.split('_')[1:]) if method not in ['user_model', 'oracle_score', 'select_score', 'mean_score'] else method + style = styles.get(method_plot, {"color": "black", "linestyle": "-"}) + plt.plot(mean_curve, label=labels.get(method_plot), **style) + + plt.fill_between( + range(len(mean_curve)), + mean_curve - 0.2 * std_curve, + mean_curve + 0.2 * std_curve, + color=style["color"], + alpha=0.2 + ) plt.xlabel("Amount of Labeled User Data", fontsize=14) plt.ylabel("RMSE", fontsize=14) - plt.title(f"Results on Homo Table Experimental Scenario", fontsize=16) + plt.title(f"Results on {task} Table Experimental Scenario", fontsize=16) plt.legend(fontsize=14) plt.tight_layout() From ed0c4bd7fd5f04b8d7ee64a149509a70917f045d Mon Sep 17 00:00:00 2001 From: liuht Date: Fri, 5 Jan 2024 21:45:24 +0800 Subject: [PATCH 09/32] [FIX] delete must test --- examples/dataset_table_workflow/base.py | 2 +- examples/dataset_table_workflow/hetero.py | 2 -- 2 files changed, 1 insertion(+), 3 deletions(-) diff --git a/examples/dataset_table_workflow/base.py b/examples/dataset_table_workflow/base.py index 1a1229e..3bcb7a8 100644 --- a/examples/dataset_table_workflow/base.py +++ b/examples/dataset_table_workflow/base.py @@ -111,7 +111,7 @@ class TableWorkflow: save_path = os.path.join(save_root_path, f"{method_name}.json") if method_name_full == "hetero_single_aug": - if test_info["force"] or recorder.should_test_method(user, idx, save_path): + if recorder.should_test_method(user, idx, save_path): # * multi-process queue = Queue() processes = [] diff --git a/examples/dataset_table_workflow/hetero.py b/examples/dataset_table_workflow/hetero.py index f365971..2f00cb9 100644 --- a/examples/dataset_table_workflow/hetero.py +++ b/examples/dataset_table_workflow/hetero.py @@ -117,7 +117,6 @@ class HeterogeneousDatasetWorkflow(TableWorkflow): def labeled_hetero_table_example(self): logger.info("Total Items: %d" % len(self.market)) methods = ["user_model", "hetero_single_aug", "hetero_multiple_avg", "hetero_ensemble_pruning"] - methods_to_test = [] recorders = {method: Recorder() for method in methods + ["select_score", "oracle_score", "mean_score"]} user = self.benchmark.name @@ -170,7 +169,6 @@ class HeterogeneousDatasetWorkflow(TableWorkflow): for method_name in methods: logger.info(f"Testing method {method_name}") test_info["method_name"] = method_name - test_info["force"] = method_name in methods_to_test test_info.update(method_configs[method_name]) self.test_method(test_info, recorders, loss_func=loss_func_rmse) From 70cc509591489ed574c9be5613e7191017ae83d0 Mon Sep 17 00:00:00 2001 From: liuht Date: Fri, 5 Jan 2024 21:53:34 +0800 Subject: [PATCH 10/32] [FIX] delete logs --- examples/dataset_table_workflow/base.py | 2 +- examples/dataset_table_workflow/train.py | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/examples/dataset_table_workflow/base.py b/examples/dataset_table_workflow/base.py index 3bcb7a8..fe35f5d 100644 --- a/examples/dataset_table_workflow/base.py +++ b/examples/dataset_table_workflow/base.py @@ -152,7 +152,7 @@ class TableWorkflow: # recorder.record(user, idx, scores) # bar.update(1) else: - if test_info["force"] or recorder.should_test_method(user, idx, save_path): + if recorder.should_test_method(user, idx, save_path): scores = self._limited_data(method, test_info, loss_func) recorder.record(user, scores) recorder.save(save_path) diff --git a/examples/dataset_table_workflow/train.py b/examples/dataset_table_workflow/train.py index c2ad170..b4f1b6c 100644 --- a/examples/dataset_table_workflow/train.py +++ b/examples/dataset_table_workflow/train.py @@ -10,7 +10,6 @@ logger = get_module_logger("train_table", level="INFO") def train_lgb(X_train, y_train, X_val, y_val, dataset): - logger.info("Training and predicting models...") model_param = user_model_params[dataset]["lgb"] params = model_param["params"] From d110218b2811085b5fa7780ac4a1d2955a36fe60 Mon Sep 17 00:00:00 2001 From: liuht Date: Sat, 6 Jan 2024 14:55:17 +0800 Subject: [PATCH 11/32] [FIX] plot function parameter mismatch --- examples/dataset_table_workflow/base.py | 6 +++--- examples/dataset_table_workflow/hetero.py | 6 +++--- examples/dataset_table_workflow/homo.py | 4 ++-- examples/dataset_table_workflow/utils.py | 20 ++++++++------------ 4 files changed, 16 insertions(+), 20 deletions(-) diff --git a/examples/dataset_table_workflow/base.py b/examples/dataset_table_workflow/base.py index fe35f5d..a9d94ee 100644 --- a/examples/dataset_table_workflow/base.py +++ b/examples/dataset_table_workflow/base.py @@ -55,8 +55,8 @@ class TableWorkflow: @staticmethod def get_train_subsets(n_labeled_list, n_repeat_list, idx, train_x, train_y): - np.random.seed(idx) - random.seed(idx) + np.random.seed(6 + idx % 3) + random.seed(6 + idx % 3) train_x = fill_data_with_mean(train_x) train_subsets = [] for n_label, repeated in zip(n_labeled_list, n_repeat_list): @@ -142,7 +142,7 @@ class TableWorkflow: recorder.record(user, all_scores) recorder.save(save_path) - process_single_aug(user, idx, recorder.data[user][idx], recorders, save_root_path) + process_single_aug(user, idx, recorder.data[user][idx], recorders, save_root_path) # * single-process # bar = tqdm(total=len(test_info["learnwares"]), desc=f"Test {method_name}") diff --git a/examples/dataset_table_workflow/hetero.py b/examples/dataset_table_workflow/hetero.py index 2f00cb9..1e33539 100644 --- a/examples/dataset_table_workflow/hetero.py +++ b/examples/dataset_table_workflow/hetero.py @@ -17,8 +17,8 @@ from utils import Recorder, plot_performance_curves logger = get_module_logger("hetero_test", level="INFO") learnware_market = ["pfs_default", "pfs_denis", "corporacion_bojan", "corporacion_lee", "corporacion_lingzhi"] default_users = ["m5_default"] # "m5_default", "m5_kkiller", "m5_rana" -n_labeled_list = [50, 75, 100, 200] -n_repeat_list = [10, 10, 10, 10] +n_labeled_list = [10, 30, 50, 75, 100, 200] +n_repeat_list = [10, 10, 10, 10, 10, 10] class HeterogeneousDatasetWorkflow(TableWorkflow): def unlabeled_hetero_table_example(self): @@ -176,5 +176,5 @@ class HeterogeneousDatasetWorkflow(TableWorkflow): recorder.save(os.path.join(self.curves_result_path, f"{user}/{user}_{method}_performance.json")) methods_to_plot = ["user_model", "select_score", "hetero_multiple_avg", "hetero_ensemble_pruning"] - plot_performance_curves(user, {method: recorders[method] for method in methods_to_plot}, task="Hetero", n_labeled_list=n_labeled_list) + plot_performance_curves(self.curves_result_path, user, {method: recorders[method] for method in methods_to_plot}, task="Hetero", n_labeled_list=n_labeled_list) \ No newline at end of file diff --git a/examples/dataset_table_workflow/homo.py b/examples/dataset_table_workflow/homo.py index 282b9a2..8277e35 100644 --- a/examples/dataset_table_workflow/homo.py +++ b/examples/dataset_table_workflow/homo.py @@ -169,7 +169,7 @@ class CorporacionDatasetWorkflow(TableWorkflow): self.test_method(test_info, recorders, loss_func=loss_func_rmse) for method, recorder in recorders.items(): - recorder.save(os.path.join(self.curves_result_path, f"{user}_{method}_performance.json")) + recorder.save(os.path.join(self.curves_result_path, f"{user}/{user}_{method}_performance.json")) methods_to_plot = ["user_model", "homo_single_aug", "homo_ensemble_pruning"] - plot_performance_curves(user, {method: recorders[method] for method in methods_to_plot}, task="Homo", n_labeled_list=homo_n_labeled_list) \ No newline at end of file + plot_performance_curves(self.curves_result_path, user, {method: recorders[method] for method in methods_to_plot}, task="Homo", n_labeled_list=homo_n_labeled_list) \ No newline at end of file diff --git a/examples/dataset_table_workflow/utils.py b/examples/dataset_table_workflow/utils.py index b14358c..120e6b9 100644 --- a/examples/dataset_table_workflow/utils.py +++ b/examples/dataset_table_workflow/utils.py @@ -69,24 +69,20 @@ def process_single_aug(user, idx, scores, recorders, root_path): logger.error(f"Error in process_single_aug for user {user}, idx {idx}: {error_message}") -def plot_performance_curves(path, users, idxs, recorders, task, n_labeled_list): +def plot_performance_curves(path, user, recorders, task, n_labeled_list): plt.figure(figsize=(10, 6)) plt.xticks(range(len(n_labeled_list)), n_labeled_list) for method, recorder in recorders.items(): - all_scores_array = [] - for user in users: - if method == "hetero_single_aug": - continue + if method == "hetero_single_aug": + continue - data_path = os.path.join(path, f"{user}_{n_labeled_list}/{user}_{method}_performance.json") - recorder.load(data_path) - scores_array = recorder.get_performance_data(user) - if scores_array: - all_scores_array.extend([scores_array[idx] for idx in idxs]) + data_path = os.path.join(path, f"{user}/{user}_{method}_performance.json") + recorder.load(data_path) + scores_array = recorder.get_performance_data(user) mean_curve, std_curve = [], [] for i in range(len(n_labeled_list)): - sub_scores_array = np.vstack([lst[i] for lst in all_scores_array]) + sub_scores_array = np.vstack([lst[i] for lst in scores_array]) sub_scores_mean = np.squeeze(np.mean(sub_scores_array, axis=0)) mean_curve.append(np.mean(sub_scores_mean)) std_curve.append(np.std(sub_scores_mean)) @@ -109,7 +105,7 @@ def plot_performance_curves(path, users, idxs, recorders, task, n_labeled_list): plt.xlabel("Amount of Labeled User Data", fontsize=14) plt.ylabel("RMSE", fontsize=14) plt.title(f"Results on {task} Table Experimental Scenario", fontsize=16) - plt.legend(fontsize=14) + plt.legend(fontsize=12) plt.tight_layout() root_path = os.path.abspath(os.path.join(__file__, "..")) From 25179c733e493c6d7789ab8800d5db8ec3d43d65 Mon Sep 17 00:00:00 2001 From: liuht Date: Sat, 6 Jan 2024 20:01:00 +0800 Subject: [PATCH 12/32] [FIX] cross_feat_eng hetero search failed --- examples/dataset_table_workflow/hetero.py | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/examples/dataset_table_workflow/hetero.py b/examples/dataset_table_workflow/hetero.py index 1e33539..ed43271 100644 --- a/examples/dataset_table_workflow/hetero.py +++ b/examples/dataset_table_workflow/hetero.py @@ -2,7 +2,6 @@ import os import warnings warnings.filterwarnings("ignore") -import json import numpy as np from learnware.logger import get_module_logger from learnware.specification import generate_stat_spec @@ -32,10 +31,15 @@ class HeterogeneousDatasetWorkflow(TableWorkflow): user = self.benchmark.name for idx in range(self.benchmark.user_num): test_x, test_y = self.benchmark.get_test_data(user_ids=idx) - test_x, test_y = test_x.values, test_y.values + test_x, test_y, feature_descriptions = test_x.values, test_y.values, test_x.columns user_stat_spec = generate_stat_spec(type="table", X=test_x) + input_description = { + "Dimension": len(feature_descriptions), + "Description": {str(i): feature_descriptions[i] for i in range(len(feature_descriptions))} + } + user_semantic["Input"] = input_description user_info = BaseUserInfo( - semantic_spec=self.user_semantic, stat_info={user_stat_spec.type: user_stat_spec} + semantic_spec=user_semantic, stat_info={user_stat_spec.type: user_stat_spec} ) logger.info(f"Searching Market for user: {user}_{idx}") @@ -48,7 +52,9 @@ class HeterogeneousDatasetWorkflow(TableWorkflow): f"single model num: {len(single_result)}, max_score: {single_result[0].score}, min_score: {single_result[-1].score}" ) - pred_y = single_result[0].learnware.predict(test_x) + single_hetero_learnware = FeatureAlignLearnware(single_result[0].learnware, **align_model_params) + single_hetero_learnware.align(user_rkme=user_stat_spec) + pred_y = single_hetero_learnware.predict(test_x) single_score_list.append(loss_func_rmse(pred_y, test_y)) rmse_list = [] From 95664a1ff5d1594af0394632e0db5a7b8891a549 Mon Sep 17 00:00:00 2001 From: liuht Date: Sat, 6 Jan 2024 21:06:59 +0800 Subject: [PATCH 13/32] [FIX] delete job selector from cross_feat_eng example --- examples/dataset_table_workflow/hetero.py | 21 +++------------------ 1 file changed, 3 insertions(+), 18 deletions(-) diff --git a/examples/dataset_table_workflow/hetero.py b/examples/dataset_table_workflow/hetero.py index ed43271..72a82d4 100644 --- a/examples/dataset_table_workflow/hetero.py +++ b/examples/dataset_table_workflow/hetero.py @@ -6,7 +6,7 @@ import numpy as np from learnware.logger import get_module_logger from learnware.specification import generate_stat_spec from learnware.market import BaseUserInfo -from learnware.reuse import AveragingReuser, JobSelectorReuser, FeatureAlignLearnware +from learnware.reuse import AveragingReuser, FeatureAlignLearnware from methods import * from base import TableWorkflow @@ -14,8 +14,6 @@ from config import align_model_params, user_semantic, hetero_n_labeled_list, het from utils import Recorder, plot_performance_curves logger = get_module_logger("hetero_test", level="INFO") -learnware_market = ["pfs_default", "pfs_denis", "corporacion_bojan", "corporacion_lee", "corporacion_lingzhi"] -default_users = ["m5_default"] # "m5_default", "m5_kkiller", "m5_rana" n_labeled_list = [10, 30, 50, 75, 100, 200] n_repeat_list = [10, 10, 10, 10, 10, 10] @@ -24,7 +22,6 @@ class HeterogeneousDatasetWorkflow(TableWorkflow): logger.info("Total Item: %d" % len(self.market)) learnware_rmse_list = [] single_score_list = [] - job_selector_score_list = [] ensemble_score_list = [] all_learnwares = self.market.get_learnwares() @@ -79,19 +76,12 @@ class HeterogeneousDatasetWorkflow(TableWorkflow): hetero_learnware = FeatureAlignLearnware(single_result[0].learnware, **align_model_params) hetero_learnware.align(user_rkme=user_stat_spec) mixture_learnware_list = [hetero_learnware] - - # test reuse (job selector) - resue_baseline = JobSelectorReuser(learnware_list=mixture_learnware_list, herding_num=100) - reuse_predict = resue_baseline.predict(user_data=test_x) - reuse_score = loss_func_rmse(reuse_predict, test_y) - job_selector_score_list.append(reuse_score) - logger.info(f"mixture reuse rmse (job selector): {reuse_score}") # test reuse (ensemble) reuse_ensemble = AveragingReuser(learnware_list=mixture_learnware_list, mode="mean") ensemble_predict_y = reuse_ensemble.predict(user_data=test_x) ensemble_score = loss_func_rmse(ensemble_predict_y, test_y) - ensemble_score_list[user].append(ensemble_score) + ensemble_score_list.append(ensemble_score) logger.info(f"mixture reuse rmse (ensemble): {ensemble_score}") learnware_rmse_list.append(rmse_list) @@ -111,10 +101,6 @@ class HeterogeneousDatasetWorkflow(TableWorkflow): np.std(oracle_score_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)) @@ -182,5 +168,4 @@ class HeterogeneousDatasetWorkflow(TableWorkflow): recorder.save(os.path.join(self.curves_result_path, f"{user}/{user}_{method}_performance.json")) methods_to_plot = ["user_model", "select_score", "hetero_multiple_avg", "hetero_ensemble_pruning"] - plot_performance_curves(self.curves_result_path, user, {method: recorders[method] for method in methods_to_plot}, task="Hetero", n_labeled_list=n_labeled_list) - \ No newline at end of file + plot_performance_curves(self.curves_result_path, user, {method: recorders[method] for method in methods_to_plot}, task="Hetero", n_labeled_list=n_labeled_list) \ No newline at end of file From 52e0d78069bbf37dbf49e4be8e510ab176f8c369 Mon Sep 17 00:00:00 2001 From: liuht Date: Thu, 11 Jan 2024 21:50:54 +0800 Subject: [PATCH 14/32] [MNT] change homo and hetero table benchmark --- examples/dataset_table_workflow/base.py | 6 +- examples/dataset_table_workflow/config.py | 331 +++++++++++--------- examples/dataset_table_workflow/hetero.py | 8 +- examples/dataset_table_workflow/homo.py | 27 +- examples/dataset_table_workflow/utils.py | 14 +- examples/dataset_table_workflow/workflow.py | 8 +- 6 files changed, 199 insertions(+), 195 deletions(-) diff --git a/examples/dataset_table_workflow/base.py b/examples/dataset_table_workflow/base.py index a9d94ee..d76c6b1 100644 --- a/examples/dataset_table_workflow/base.py +++ b/examples/dataset_table_workflow/base.py @@ -30,7 +30,7 @@ class TableWorkflow: def __init__(self, benchmark_config, name="easy", rebuild=False): self.root_path = os.path.abspath(os.path.join(__file__, "..")) self.result_path = os.path.join(self.root_path, "results") - self.curves_result_path = os.path.join(self.root_path, "curves") + self.curves_result_path = os.path.join(self.result_path, "curves") os.makedirs(self.result_path, exist_ok=True) os.makedirs(self.curves_result_path, exist_ok=True) self._prepare_market(benchmark_config, name, rebuild) @@ -55,8 +55,8 @@ class TableWorkflow: @staticmethod def get_train_subsets(n_labeled_list, n_repeat_list, idx, train_x, train_y): - np.random.seed(6 + idx % 3) - random.seed(6 + idx % 3) + np.random.seed(idx) + random.seed(idx) train_x = fill_data_with_mean(train_x) train_subsets = [] for n_label, repeated in zip(n_labeled_list, n_repeat_list): diff --git a/examples/dataset_table_workflow/config.py b/examples/dataset_table_workflow/config.py index 33c060e..49931e7 100644 --- a/examples/dataset_table_workflow/config.py +++ b/examples/dataset_table_workflow/config.py @@ -3,8 +3,8 @@ from learnware.tests.benchmarks import BenchmarkConfig homo_n_labeled_list = [100, 200, 500, 1000, 2000, 4000, 6000, 8000, 10000] homo_n_repeat_list = [10, 10, 10, 3, 3, 3, 3, 3, 3] -hetero_n_labeled_list = [10, 30, 50, 75, 100, 200] -hetero_n_repeat_list = [10, 10, 10, 10, 10, 10] +hetero_n_labeled_list = [10, 30, 50, 75, 100, 200, 500, 1000, 2000] +hetero_n_repeat_list = [10, 10, 10, 10, 10, 10, 3, 3, 3] user_semantic = { @@ -114,104 +114,123 @@ user_model_params = { "MAX_ROUNDS": 1000, "early_stopping_rounds": 1000 } + }, + "PFS_HOMO": { + "lgb": { + "params": { + "boosting_type": "gbdt", + "num_leaves": 2**7 - 1, + "learning_rate": 0.01, + "objective": "rmse", + "metric": "rmse", + "feature_fraction": 0.75, + "bagging_fraction": 0.75, + "bagging_freq": 5, + "seed": 1, + "verbose": -100, + "n_estimators": 100000, + }, + "MAX_ROUNDS": 1000, + "early_stopping_rounds": 1000 + } } } homo_table_benchmark_config = BenchmarkConfig( - name="Corporacion", - user_num=54, + name="PFS_HOMO", + user_num=53, learnware_ids=[ - "00000912", "00000911", "00000910", "00000909", - "00000908", "00000907", "00000906", "00000905", - "00000904", "00000903", "00000902", "00000901", - "00000900", "00000899", "00000898", "00000897", - "00000896", "00000895", "00000894", "00000893", - "00000892", "00000891", "00000890", "00000889", - "00000888", "00000887", "00000886", "00000885", - "00000884", "00000883", "00000882", "00000881", - "00000880", "00000879", "00000878", "00000877", - "00000876", "00000875", "00000874", "00000873", - "00000872", "00000871", "00000870", "00000869", - "00000868", "00000867", "00000866", "00000865", - "00000864", "00000863", "00000862", "00000861", - "00000860", "00000859" + "00002265", "00002266", "00002267", "00002268", + "00002269", "00002270", "00002271", "00002272", + "00002273", "00002274", "00002275", "00002276", + "00002277", "00002278", "00002279", "00002280", + "00002281", "00002282", "00002283", "00002284", + "00002285", "00002286", "00002287", "00002288", + "00002289", "00002290", "00002291", "00002292", + "00002293", "00002294", "00002295", "00002296", + "00002297", "00002298", "00002299", "00002300", + "00002301", "00002302", "00002303", "00002304", + "00002305", "00002306", "00002307", "00002308", + "00002309", "00002310", "00002311", "00002312", + "00002313", "00002314", "00002315", "00002316", + "00002317" ], - test_data_path="Corporacion/test_data.zip", - train_data_path="Corporacion/train_data.zip", - extra_info_path="Corporacion/extra_info.zip" + test_data_path="PFS_HOMO/test_data.zip", + train_data_path="PFS_HOMO/train_data.zip", + extra_info_path="PFS_HOMO/extra_info.zip" ) hetero_cross_feat_eng_benchmark_config = BenchmarkConfig( name="PFS", user_num=41, learnware_ids = [ - "00000394", "00000393", "00000392", "00000391", - "00000390", "00000389", "00000388", "00000387", - "00000386", "00000385", "00000384", "00000383", - "00000382", "00000381", "00000380", "00000379", - "00000378", "00000377", "00000376", "00000375", - "00000374", "00000373", "00000372", "00000371", - "00000370", "00000369", "00000368", "00000367", - "00000366", "00000365", "00000364", "00000363", - "00000362", "00000361", "00000360", "00000359", - "00000358", "00000357", "00000356", "00000355", - "00000354", "00000353", "00000352", "00000351", - "00000350", "00000349", "00000348", "00000347", - "00000346", "00000345", "00000344", "00000343", - "00000342", "00000444", "00000443", "00000442", - "00000441", "00000440", "00000439", "00000438", - "00000437", "00000436", "00000435", "00000434", - "00000433", "00000432", "00000431", "00000430", - "00000429", "00000428", "00000427", "00000426", - "00000425", "00000424", "00000423", "00000422", - "00000421", "00000420", "00000419", "00000418", - "00000417", "00000416", "00000415", "00000414", - "00000413", "00000412", "00000411", "00000410", - "00000409", "00000408", "00000407", "00000406", - "00000405", "00000404", "00000403", "00000402", - "00000401", "00000400", "00000399", "00000398", - "00000397", "00000396", "00000395", "00000783", - "00000782", "00000781", "00000780", "00000779", - "00000778", "00000777", "00000776", "00000775", - "00000774", "00000773", "00000772", "00000771", - "00000770", "00000769", "00000768", "00000767", - "00000766", "00000765", "00000764", "00000763", - "00000762", "00000761", "00000760", "00000759", - "00000758", "00000757", "00000756", "00000755", - "00000754", "00000753", "00000752", "00000751", - "00000750", "00000749", "00000748", "00000747", - "00000746", "00000745", "00000744", "00000743", - "00000742", "00000741", "00000740", "00000739", - "00000738", "00000737", "00000736", "00000735", - "00000734", "00000733", "00000732", "00000731", - "00000730", "00000839", "00000838", "00000837", - "00000836", "00000835", "00000834", "00000833", - "00000832", "00000831", "00000830", "00000829", - "00000828", "00000827", "00000826", "00000825", - "00000824", "00000823", "00000822", "00000821", - "00000820", "00000819", "00000818", "00000817", - "00000816", "00000815", "00000814", "00000813", - "00000812", "00000811", "00000810", "00000809", - "00000808", "00000807", "00000806", "00000805", - "00000804", "00000803", "00000802", "00000801", - "00000800", "00000799", "00000798", "00000797", - "00000796", "00000795", "00000794", "00000793", - "00000792", "00000791", "00000790", "00000789", - "00000788", "00000787", "00000786", "00000912", - "00000911", "00000910", "00000909", "00000908", - "00000907", "00000906", "00000905", "00000904", - "00000903", "00000902", "00000901", "00000900", - "00000899", "00000898", "00000897", "00000896", - "00000895", "00000894", "00000893", "00000892", - "00000891", "00000890", "00000889", "00000888", - "00000887", "00000886", "00000885", "00000884", - "00000883", "00000882", "00000881", "00000880", - "00000879", "00000878", "00000877", "00000876", - "00000875", "00000874", "00000873", "00000872", - "00000871", "00000870", "00000869", "00000868", - "00000867", "00000866", "00000865", "00000864", - "00000863", "00000862", "00000861", "00000860", - "00000859" + "00000342", "00000343", "00000344", "00000345", + "00000346", "00000347", "00000348", "00000349", + "00000350", "00000351", "00000352", "00000353", + "00000354", "00000355", "00000356", "00000357", + "00000358", "00000359", "00000360", "00000361", + "00000362", "00000363", "00000364", "00000365", + "00000366", "00000367", "00000368", "00000369", + "00000370", "00000371", "00000372", "00000373", + "00000374", "00000375", "00000376", "00000377", + "00000378", "00000379", "00000380", "00000381", + "00000382", "00000383", "00000384", "00000385", + "00000386", "00000387", "00000388", "00000389", + "00000390", "00000391", "00000392", "00000393", + "00000394", "00000395", "00000396", "00000397", + "00000398", "00000399", "00000400", "00000401", + "00000402", "00000403", "00000404", "00000405", + "00000406", "00000407", "00000408", "00000409", + "00000410", "00000411", "00000412", "00000413", + "00000414", "00000415", "00000416", "00000417", + "00000418", "00000419", "00000420", "00000421", + "00000422", "00000423", "00000424", "00000425", + "00000426", "00000427", "00000428", "00000429", + "00000430", "00000431", "00000432", "00000433", + "00000434", "00000435", "00000436", "00000437", + "00000438", "00000439", "00000440", "00000441", + "00000442", "00000443", "00000444", "00000730", + "00000731", "00000732", "00000733", "00000734", + "00000735", "00000736", "00000737", "00000738", + "00000739", "00000740", "00000741", "00000742", + "00000743", "00000744", "00000745", "00000746", + "00000747", "00000748", "00000749", "00000750", + "00000751", "00000752", "00000753", "00000754", + "00000755", "00000756", "00000757", "00000758", + "00000759", "00000760", "00000761", "00000762", + "00000763", "00000764", "00000765", "00000766", + "00000767", "00000768", "00000769", "00000770", + "00000771", "00000772", "00000773", "00000774", + "00000775", "00000776", "00000777", "00000778", + "00000779", "00000780", "00000781", "00000782", + "00000783", "00000786", "00000787", "00000788", + "00000789", "00000790", "00000791", "00000792", + "00000793", "00000794", "00000795", "00000796", + "00000797", "00000798", "00000799", "00000800", + "00000801", "00000802", "00000803", "00000804", + "00000805", "00000806", "00000807", "00000808", + "00000809", "00000810", "00000811", "00000812", + "00000813", "00000814", "00000815", "00000816", + "00000817", "00000818", "00000819", "00000820", + "00000821", "00000822", "00000823", "00000824", + "00000825", "00000826", "00000827", "00000828", + "00000829", "00000830", "00000831", "00000832", + "00000833", "00000834", "00000835", "00000836", + "00000837", "00000838", "00000839", "00000859", + "00000860", "00000861", "00000862", "00000863", + "00000864", "00000865", "00000866", "00000867", + "00000868", "00000869", "00000870", "00000871", + "00000872", "00000873", "00000874", "00000875", + "00000876", "00000877", "00000878", "00000879", + "00000880", "00000881", "00000882", "00000883", + "00000884", "00000885", "00000886", "00000887", + "00000888", "00000889", "00000890", "00000891", + "00000892", "00000893", "00000894", "00000895", + "00000896", "00000897", "00000898", "00000899", + "00000900", "00000901", "00000902", "00000903", + "00000904", "00000905", "00000906", "00000907", + "00000908", "00000909", "00000910", "00000911", + "00000912" ], test_data_path="PFS/test_data.zip", train_data_path="PFS/train_data.zip", @@ -220,75 +239,75 @@ hetero_cross_feat_eng_benchmark_config = BenchmarkConfig( hetero_cross_task_benchmark_config = BenchmarkConfig( name="M5", - user_num=9, + user_num=30, learnware_ids = [ - "00000394", "00000393", "00000392", "00000391", - "00000390", "00000389", "00000388", "00000387", - "00000386", "00000385", "00000384", "00000383", - "00000382", "00000381", "00000380", "00000379", - "00000378", "00000377", "00000376", "00000375", - "00000374", "00000373", "00000372", "00000371", - "00000370", "00000369", "00000368", "00000367", - "00000366", "00000365", "00000364", "00000363", - "00000362", "00000361", "00000360", "00000359", - "00000358", "00000357", "00000356", "00000355", - "00000354", "00000353", "00000352", "00000351", - "00000350", "00000349", "00000348", "00000347", - "00000346", "00000345", "00000344", "00000343", - "00000342", "00000444", "00000443", "00000442", - "00000441", "00000440", "00000439", "00000438", - "00000437", "00000436", "00000435", "00000434", - "00000433", "00000432", "00000431", "00000430", - "00000429", "00000428", "00000427", "00000426", - "00000425", "00000424", "00000423", "00000422", - "00000421", "00000420", "00000419", "00000418", - "00000417", "00000416", "00000415", "00000414", - "00000413", "00000412", "00000411", "00000410", - "00000409", "00000408", "00000407", "00000406", - "00000405", "00000404", "00000403", "00000402", - "00000401", "00000400", "00000399", "00000398", - "00000397", "00000396", "00000395", "00000783", - "00000782", "00000781", "00000780", "00000779", - "00000778", "00000777", "00000776", "00000775", - "00000774", "00000773", "00000772", "00000771", - "00000770", "00000769", "00000768", "00000767", - "00000766", "00000765", "00000764", "00000763", - "00000762", "00000761", "00000760", "00000759", - "00000758", "00000757", "00000756", "00000755", - "00000754", "00000753", "00000752", "00000751", - "00000750", "00000749", "00000748", "00000747", - "00000746", "00000745", "00000744", "00000743", - "00000742", "00000741", "00000740", "00000739", - "00000738", "00000737", "00000736", "00000735", - "00000734", "00000733", "00000732", "00000731", - "00000730", "00000839", "00000838", "00000837", - "00000836", "00000835", "00000834", "00000833", - "00000832", "00000831", "00000830", "00000829", - "00000828", "00000827", "00000826", "00000825", - "00000824", "00000823", "00000822", "00000821", - "00000820", "00000819", "00000818", "00000817", - "00000816", "00000815", "00000814", "00000813", - "00000812", "00000811", "00000810", "00000809", - "00000808", "00000807", "00000806", "00000805", - "00000804", "00000803", "00000802", "00000801", - "00000800", "00000799", "00000798", "00000797", - "00000796", "00000795", "00000794", "00000793", - "00000792", "00000791", "00000790", "00000789", - "00000788", "00000787", "00000786", "00000912", - "00000911", "00000910", "00000909", "00000908", - "00000907", "00000906", "00000905", "00000904", - "00000903", "00000902", "00000901", "00000900", - "00000899", "00000898", "00000897", "00000896", - "00000895", "00000894", "00000893", "00000892", - "00000891", "00000890", "00000889", "00000888", - "00000887", "00000886", "00000885", "00000884", - "00000883", "00000882", "00000881", "00000880", - "00000879", "00000878", "00000877", "00000876", - "00000875", "00000874", "00000873", "00000872", - "00000871", "00000870", "00000869", "00000868", - "00000867", "00000866", "00000865", "00000864", - "00000863", "00000862", "00000861", "00000860", - "00000859" + "00000342", "00000343", "00000344", "00000345", + "00000346", "00000347", "00000348", "00000349", + "00000350", "00000351", "00000352", "00000353", + "00000354", "00000355", "00000356", "00000357", + "00000358", "00000359", "00000360", "00000361", + "00000362", "00000363", "00000364", "00000365", + "00000366", "00000367", "00000368", "00000369", + "00000370", "00000371", "00000372", "00000373", + "00000374", "00000375", "00000376", "00000377", + "00000378", "00000379", "00000380", "00000381", + "00000382", "00000383", "00000384", "00000385", + "00000386", "00000387", "00000388", "00000389", + "00000390", "00000391", "00000392", "00000393", + "00000394", "00000395", "00000396", "00000397", + "00000398", "00000399", "00000400", "00000401", + "00000402", "00000403", "00000404", "00000405", + "00000406", "00000407", "00000408", "00000409", + "00000410", "00000411", "00000412", "00000413", + "00000414", "00000415", "00000416", "00000417", + "00000418", "00000419", "00000420", "00000421", + "00000422", "00000423", "00000424", "00000425", + "00000426", "00000427", "00000428", "00000429", + "00000430", "00000431", "00000432", "00000433", + "00000434", "00000435", "00000436", "00000437", + "00000438", "00000439", "00000440", "00000441", + "00000442", "00000443", "00000444", "00000730", + "00000731", "00000732", "00000733", "00000734", + "00000735", "00000736", "00000737", "00000738", + "00000739", "00000740", "00000741", "00000742", + "00000743", "00000744", "00000745", "00000746", + "00000747", "00000748", "00000749", "00000750", + "00000751", "00000752", "00000753", "00000754", + "00000755", "00000756", "00000757", "00000758", + "00000759", "00000760", "00000761", "00000762", + "00000763", "00000764", "00000765", "00000766", + "00000767", "00000768", "00000769", "00000770", + "00000771", "00000772", "00000773", "00000774", + "00000775", "00000776", "00000777", "00000778", + "00000779", "00000780", "00000781", "00000782", + "00000783", "00000786", "00000787", "00000788", + "00000789", "00000790", "00000791", "00000792", + "00000793", "00000794", "00000795", "00000796", + "00000797", "00000798", "00000799", "00000800", + "00000801", "00000802", "00000803", "00000804", + "00000805", "00000806", "00000807", "00000808", + "00000809", "00000810", "00000811", "00000812", + "00000813", "00000814", "00000815", "00000816", + "00000817", "00000818", "00000819", "00000820", + "00000821", "00000822", "00000823", "00000824", + "00000825", "00000826", "00000827", "00000828", + "00000829", "00000830", "00000831", "00000832", + "00000833", "00000834", "00000835", "00000836", + "00000837", "00000838", "00000839", "00000859", + "00000860", "00000861", "00000862", "00000863", + "00000864", "00000865", "00000866", "00000867", + "00000868", "00000869", "00000870", "00000871", + "00000872", "00000873", "00000874", "00000875", + "00000876", "00000877", "00000878", "00000879", + "00000880", "00000881", "00000882", "00000883", + "00000884", "00000885", "00000886", "00000887", + "00000888", "00000889", "00000890", "00000891", + "00000892", "00000893", "00000894", "00000895", + "00000896", "00000897", "00000898", "00000899", + "00000900", "00000901", "00000902", "00000903", + "00000904", "00000905", "00000906", "00000907", + "00000908", "00000909", "00000910", "00000911", + "00000912" ], test_data_path="M5/test_data.zip", train_data_path="M5/train_data.zip", diff --git a/examples/dataset_table_workflow/hetero.py b/examples/dataset_table_workflow/hetero.py index 72a82d4..d8a9ec2 100644 --- a/examples/dataset_table_workflow/hetero.py +++ b/examples/dataset_table_workflow/hetero.py @@ -14,8 +14,7 @@ from config import align_model_params, user_semantic, hetero_n_labeled_list, het from utils import Recorder, plot_performance_curves logger = get_module_logger("hetero_test", level="INFO") -n_labeled_list = [10, 30, 50, 75, 100, 200] -n_repeat_list = [10, 10, 10, 10, 10, 10] + class HeterogeneousDatasetWorkflow(TableWorkflow): def unlabeled_hetero_table_example(self): @@ -106,6 +105,7 @@ class HeterogeneousDatasetWorkflow(TableWorkflow): % (np.mean(ensemble_score_list), np.std(ensemble_score_list)) ) + def labeled_hetero_table_example(self): logger.info("Total Items: %d" % len(self.market)) methods = ["user_model", "hetero_single_aug", "hetero_multiple_avg", "hetero_ensemble_pruning"] @@ -167,5 +167,5 @@ class HeterogeneousDatasetWorkflow(TableWorkflow): for method, recorder in recorders.items(): recorder.save(os.path.join(self.curves_result_path, f"{user}/{user}_{method}_performance.json")) - methods_to_plot = ["user_model", "select_score", "hetero_multiple_avg", "hetero_ensemble_pruning"] - plot_performance_curves(self.curves_result_path, user, {method: recorders[method] for method in methods_to_plot}, task="Hetero", n_labeled_list=n_labeled_list) \ No newline at end of file + methods_to_plot = ["user_model", "select_score", "mean_score", "hetero_multiple_avg", "hetero_ensemble_pruning"] + plot_performance_curves(self.curves_result_path, user, {method: recorders[method] for method in methods_to_plot}, task="Hetero", n_labeled_list=hetero_n_labeled_list) \ No newline at end of file diff --git a/examples/dataset_table_workflow/homo.py b/examples/dataset_table_workflow/homo.py index 8277e35..0090cca 100644 --- a/examples/dataset_table_workflow/homo.py +++ b/examples/dataset_table_workflow/homo.py @@ -16,14 +16,13 @@ from utils import Recorder, plot_performance_curves logger = get_module_logger("homo_table", level="INFO") -class CorporacionDatasetWorkflow(TableWorkflow): +class HomogeneousDatasetWorkflow(TableWorkflow): def unlabeled_homo_table_example(self): logger.info("Total Item: %d" % (len(self.market))) learnware_rmse_list = [] single_score_list = [] job_selector_score_list = [] ensemble_score_list = [] - pruning_score_list = [] all_learnwares = self.market.get_learnwares() user = self.benchmark.name @@ -36,7 +35,7 @@ class CorporacionDatasetWorkflow(TableWorkflow): ) logger.info(f"Searching Market for user: {user}_{idx}") - search_result = self.market.search_learnware(user_info) + search_result = self.market.search_learnware(user_info, max_search_num=2) single_result = search_result.get_single_results() multiple_result = search_result.get_multiple_results() @@ -50,8 +49,10 @@ class CorporacionDatasetWorkflow(TableWorkflow): rmse_list = [] for learnware in all_learnwares: - pred_y = learnware.predict(test_x) - rmse_list.append(loss_func_rmse(pred_y, test_y)) + semantic_spec = learnware.specification.get_semantic_spec() + if semantic_spec["Input"]["Dimension"] == test_x.shape[1]: + pred_y = learnware.predict(test_x) + rmse_list.append(loss_func_rmse(pred_y, test_y)) logger.info( f"Top1-score: {single_result[0].score}, learnware_id: {single_result[0].learnware.id}, rmse: {single_score_list[-1]}" ) @@ -76,14 +77,7 @@ class CorporacionDatasetWorkflow(TableWorkflow): ensemble_score = loss_func_rmse(ensemble_predict_y, test_y) ensemble_score_list.append(ensemble_score) logger.info(f"mixture reuse rmse (ensemble): {ensemble_score}") - - # test reuse (ensemblePruning) - reuse_pruning = EnsemblePruningReuser(learnware_list=mixture_learnware_list, mode="regression") - pruning_predict_y = reuse_pruning.predict(user_data=test_x) - pruning_score = loss_func_rmse(pruning_predict_y, test_y) - pruning_score_list.append(pruning_score) - logger.info(f"mixture reuse rmse (ensemble Pruning): {pruning_score}\n") - + learnware_rmse_list.append(rmse_list) single_list = np.array(learnware_rmse_list) @@ -109,11 +103,8 @@ class CorporacionDatasetWorkflow(TableWorkflow): "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)) - ) - + + def labeled_homo_table_example(self): logger.info("Total Item: %d" % (len(self.market))) methods = ["user_model", "homo_single_aug", "homo_multiple_aug", "homo_multiple_avg", "homo_ensemble_pruning"] diff --git a/examples/dataset_table_workflow/utils.py b/examples/dataset_table_workflow/utils.py index 120e6b9..1e23e8e 100644 --- a/examples/dataset_table_workflow/utils.py +++ b/examples/dataset_table_workflow/utils.py @@ -41,15 +41,10 @@ class Recorder: def process_single_aug(user, idx, scores, recorders, root_path): try: - scores_array = np.array(scores) - n_labeled = len(scores_array[0]) - - while scores_array.ndim < 3: - scores_array = scores_array[np.newaxis, :] - + n_labeled = len(scores[0]) select_scores, mean_scores, oracle_scores = [], [], [] for i in range(n_labeled): - sub_scores_array = np.vstack([lst[i] for lst in scores_array]) + sub_scores_array = np.vstack([lst[i] for lst in scores]) sub_scores_select = np.squeeze(sub_scores_array[0]) sub_scores_mean = np.squeeze(np.mean(sub_scores_array, axis=0)) sub_scores_min = np.squeeze(np.min(sub_scores_array, axis=0)) @@ -63,7 +58,6 @@ def process_single_aug(user, idx, scores, recorders, root_path): recorders[method_name].record(user, scores) save_path = os.path.join(root_path, f"{method_name}.json") recorders[method_name].save(save_path) - except Exception: error_message = traceback.format_exc() logger.error(f"Error in process_single_aug for user {user}, idx {idx}: {error_message}") @@ -96,8 +90,8 @@ def plot_performance_curves(path, user, recorders, task, n_labeled_list): plt.fill_between( range(len(mean_curve)), - mean_curve - 0.2 * std_curve, - mean_curve + 0.2 * std_curve, + mean_curve - std_curve, + mean_curve + std_curve, color=style["color"], alpha=0.2 ) diff --git a/examples/dataset_table_workflow/workflow.py b/examples/dataset_table_workflow/workflow.py index 902e9fd..08a7527 100644 --- a/examples/dataset_table_workflow/workflow.py +++ b/examples/dataset_table_workflow/workflow.py @@ -1,7 +1,7 @@ import fire from learnware.logger import get_module_logger -from homo import CorporacionDatasetWorkflow +from homo import HomogeneousDatasetWorkflow from hetero import HeterogeneousDatasetWorkflow from config import homo_table_benchmark_config, hetero_cross_feat_eng_benchmark_config, hetero_cross_task_benchmark_config @@ -10,15 +10,15 @@ logger = get_module_logger("base_table", level="INFO") class TableDatasetWorkflow: def unlabeled_homo_table_example(self): - workflow = CorporacionDatasetWorkflow( + workflow = HomogeneousDatasetWorkflow( benchmark_config=homo_table_benchmark_config, name="easy", - rebuild=False + rebuild=True ) workflow.unlabeled_homo_table_example() def labeled_homo_table_example(self): - workflow = CorporacionDatasetWorkflow( + workflow = HomogeneousDatasetWorkflow( benchmark_config=homo_table_benchmark_config, name="easy", rebuild=False From 38fef2741a2b736f3748992906c3051e6cee7c06 Mon Sep 17 00:00:00 2001 From: liuht Date: Fri, 12 Jan 2024 00:50:31 +0800 Subject: [PATCH 15/32] [MNT] market engine local train --- examples/dataset_table_workflow/base.py | 39 +++++++++++++-------- examples/dataset_table_workflow/workflow.py | 6 ++-- 2 files changed, 29 insertions(+), 16 deletions(-) diff --git a/examples/dataset_table_workflow/base.py b/examples/dataset_table_workflow/base.py index d76c6b1..040ea54 100644 --- a/examples/dataset_table_workflow/base.py +++ b/examples/dataset_table_workflow/base.py @@ -2,7 +2,9 @@ import os import time import torch import random +import requests import tempfile +import traceback import numpy as np from queue import Empty from tqdm import tqdm @@ -27,13 +29,13 @@ torch.multiprocessing.set_sharing_strategy("file_system") class TableWorkflow: - def __init__(self, benchmark_config, name="easy", rebuild=False): + def __init__(self, benchmark_config, name="easy", rebuild=False, retrain=False): self.root_path = os.path.abspath(os.path.join(__file__, "..")) self.result_path = os.path.join(self.root_path, "results") self.curves_result_path = os.path.join(self.result_path, "curves") os.makedirs(self.result_path, exist_ok=True) os.makedirs(self.curves_result_path, exist_ok=True) - self._prepare_market(benchmark_config, name, rebuild) + self._prepare_market(benchmark_config, name, rebuild, retrain) self.cuda_idx = list(range(torch.cuda.device_count())) @@ -68,13 +70,21 @@ class TableWorkflow: train_subsets[-1].append({"x_train": np.array(train_x[subset_idxs]), "y_train": np.array(train_y[subset_idxs])}) return train_subsets - def _prepare_market(self, benchmark_config, name, rebuild): + def _prepare_market(self, benchmark_config, name, rebuild, retrain): client = LearnwareClient() self.benchmark = LearnwareBenchmark().get_benchmark(benchmark_config) - self.market = instantiate_learnware_market(market_id=self.benchmark.name, name=name, rebuild=rebuild) + self.market = instantiate_learnware_market( + market_id=self.benchmark.name, + name=name, + rebuild=rebuild, + organizer_kwargs={ + "auto_update": True, + "auto_update_limit": len(self.benchmark.learnware_ids), + **market_mapping_params + } if retrain else None + ) self.user_semantic = client.get_semantic_specification(self.benchmark.learnware_ids[0]) self.user_semantic["Name"]["Values"] = "" - if len(self.market) == 0 or rebuild == True: for learnware_id in self.benchmark.learnware_ids: with tempfile.TemporaryDirectory(prefix="table_benchmark_") as tempdir: @@ -85,7 +95,8 @@ class TableWorkflow: client.download_learnware(learnware_id, zip_path) self.market.add_learnware(zip_path, semantic_spec) break - except: + except (requests.exceptions.RequestException, IOError, Exception) as e: + logger.info(f"An error occurred when downloading {learnware_id}: {e}\n{traceback.format_exc()}, retrying...") time.sleep(1) continue @@ -112,6 +123,14 @@ class TableWorkflow: if method_name_full == "hetero_single_aug": if recorder.should_test_method(user, idx, save_path): + # * single-process + # bar = tqdm(total=len(test_info["learnwares"]), desc=f"Test {method_name}") + # for learnware in test_info['learnwares']: + # test_info['single_learnware'] = learnware + # scores = self._limited_data(test_methods[method_name_full], test_info, loss_func) + # recorder.record(user, idx, scores) + # bar.update(1) + # * multi-process queue = Queue() processes = [] @@ -143,14 +162,6 @@ class TableWorkflow: recorder.save(save_path) process_single_aug(user, idx, recorder.data[user][idx], recorders, save_root_path) - - # * single-process - # bar = tqdm(total=len(test_info["learnwares"]), desc=f"Test {method_name}") - # for learnware in test_info['learnwares']: - # test_info['single_learnware'] = learnware - # scores = self._limited_data(test_methods[method_name_full], test_info, loss_func) - # recorder.record(user, idx, scores) - # bar.update(1) else: if recorder.should_test_method(user, idx, save_path): scores = self._limited_data(method, test_info, loss_func) diff --git a/examples/dataset_table_workflow/workflow.py b/examples/dataset_table_workflow/workflow.py index 08a7527..73ebc86 100644 --- a/examples/dataset_table_workflow/workflow.py +++ b/examples/dataset_table_workflow/workflow.py @@ -29,7 +29,8 @@ class TableDatasetWorkflow: workflow = HeterogeneousDatasetWorkflow( benchmark_config=hetero_cross_feat_eng_benchmark_config, name="hetero", - rebuild=True + rebuild=False, + retrain=False ) workflow.unlabeled_hetero_table_example() @@ -37,7 +38,8 @@ class TableDatasetWorkflow: workflow = HeterogeneousDatasetWorkflow( benchmark_config=hetero_cross_task_benchmark_config, name="hetero", - rebuild=False + rebuild=True, + retrain=True ) workflow.labeled_hetero_table_example() From e565af1f18d3817079ed3c2113364244c2db4b6a Mon Sep 17 00:00:00 2001 From: liuht Date: Fri, 12 Jan 2024 21:53:18 +0800 Subject: [PATCH 16/32] [FIX] unify train subset random seed --- examples/dataset_table_workflow/base.py | 6 ++-- examples/dataset_table_workflow/config.py | 34 ++++++++++----------- examples/dataset_table_workflow/hetero.py | 2 +- examples/dataset_table_workflow/homo.py | 6 ++-- examples/dataset_table_workflow/workflow.py | 4 +-- 5 files changed, 26 insertions(+), 26 deletions(-) diff --git a/examples/dataset_table_workflow/base.py b/examples/dataset_table_workflow/base.py index 040ea54..93a66cd 100644 --- a/examples/dataset_table_workflow/base.py +++ b/examples/dataset_table_workflow/base.py @@ -56,9 +56,9 @@ class TableWorkflow: return all_scores @staticmethod - def get_train_subsets(n_labeled_list, n_repeat_list, idx, train_x, train_y): - np.random.seed(idx) - random.seed(idx) + def get_train_subsets(n_labeled_list, n_repeat_list, train_x, train_y): + np.random.seed(1) + random.seed(1) train_x = fill_data_with_mean(train_x) train_subsets = [] for n_label, repeated in zip(n_labeled_list, n_repeat_list): diff --git a/examples/dataset_table_workflow/config.py b/examples/dataset_table_workflow/config.py index 49931e7..a23cc31 100644 --- a/examples/dataset_table_workflow/config.py +++ b/examples/dataset_table_workflow/config.py @@ -61,23 +61,23 @@ market_mapping_params = { } user_model_params = { - "Corporacion": { - "lgb": { - "params": { - "num_leaves": 31, - "objective": "regression", - "learning_rate": 0.1, - "feature_fraction": 0.8, - "bagging_fraction": 0.8, - "bagging_freq": 2, - "metric": "l2", - "num_threads": 4, - "verbose": -1, - }, - "MAX_ROUNDS": 500, - "early_stopping_rounds": 50, - } - }, + # "Corporacion": { + # "lgb": { + # "params": { + # "num_leaves": 31, + # "objective": "regression", + # "learning_rate": 0.1, + # "feature_fraction": 0.8, + # "bagging_fraction": 0.8, + # "bagging_freq": 2, + # "metric": "l2", + # "num_threads": 4, + # "verbose": -1, + # }, + # "MAX_ROUNDS": 500, + # "early_stopping_rounds": 50, + # } + # }, "M5": { "lgb": { "params": { diff --git a/examples/dataset_table_workflow/hetero.py b/examples/dataset_table_workflow/hetero.py index d8a9ec2..21b87e9 100644 --- a/examples/dataset_table_workflow/hetero.py +++ b/examples/dataset_table_workflow/hetero.py @@ -118,7 +118,7 @@ class HeterogeneousDatasetWorkflow(TableWorkflow): train_x, train_y = self.benchmark.get_train_data(user_ids=idx) train_x, train_y, feature_descriptions = train_x.values, train_y.values, train_x.columns - train_subsets = self.get_train_subsets(hetero_n_labeled_list, hetero_n_repeat_list, idx, train_x, train_y) + train_subsets = self.get_train_subsets(hetero_n_labeled_list, hetero_n_repeat_list, train_x, train_y) user_stat_spec = generate_stat_spec(type="table", X=test_x) input_description = { diff --git a/examples/dataset_table_workflow/homo.py b/examples/dataset_table_workflow/homo.py index 0090cca..531ebe9 100644 --- a/examples/dataset_table_workflow/homo.py +++ b/examples/dataset_table_workflow/homo.py @@ -107,7 +107,7 @@ class HomogeneousDatasetWorkflow(TableWorkflow): def labeled_homo_table_example(self): logger.info("Total Item: %d" % (len(self.market))) - methods = ["user_model", "homo_single_aug", "homo_multiple_aug", "homo_multiple_avg", "homo_ensemble_pruning"] + methods = ["user_model", "homo_single_aug", "homo_multiple_avg", "homo_ensemble_pruning"] methods_to_retest = [] recorders = {method: Recorder() for method in methods} @@ -118,7 +118,7 @@ class HomogeneousDatasetWorkflow(TableWorkflow): train_x, train_y = self.benchmark.get_train_data(user_ids=idx) train_x, train_y = train_x.values, train_y.values - train_subsets = self.get_train_subsets(homo_n_labeled_list, homo_n_repeat_list, idx, train_x, train_y) + train_subsets = self.get_train_subsets(homo_n_labeled_list, homo_n_repeat_list, train_x, train_y) user_stat_spec = generate_stat_spec(type="table", X=test_x) user_info = BaseUserInfo( @@ -162,5 +162,5 @@ class HomogeneousDatasetWorkflow(TableWorkflow): for method, recorder in recorders.items(): recorder.save(os.path.join(self.curves_result_path, f"{user}/{user}_{method}_performance.json")) - methods_to_plot = ["user_model", "homo_single_aug", "homo_ensemble_pruning"] + methods_to_plot = ["user_model", "homo_single_aug", "homo_multiple_avg", "homo_ensemble_pruning"] plot_performance_curves(self.curves_result_path, user, {method: recorders[method] for method in methods_to_plot}, task="Homo", n_labeled_list=homo_n_labeled_list) \ No newline at end of file diff --git a/examples/dataset_table_workflow/workflow.py b/examples/dataset_table_workflow/workflow.py index 73ebc86..5dbe80d 100644 --- a/examples/dataset_table_workflow/workflow.py +++ b/examples/dataset_table_workflow/workflow.py @@ -38,8 +38,8 @@ class TableDatasetWorkflow: workflow = HeterogeneousDatasetWorkflow( benchmark_config=hetero_cross_task_benchmark_config, name="hetero", - rebuild=True, - retrain=True + rebuild=False, + retrain=False ) workflow.labeled_hetero_table_example() From b89604fc5e99e62fcf195638fd785efd2cc13c17 Mon Sep 17 00:00:00 2001 From: liuht Date: Fri, 12 Jan 2024 22:07:57 +0800 Subject: [PATCH 17/32] [FIX] delete homo_multiple_avg from unlabeled_homo_table_example --- examples/dataset_table_workflow/homo.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/examples/dataset_table_workflow/homo.py b/examples/dataset_table_workflow/homo.py index 531ebe9..73c0544 100644 --- a/examples/dataset_table_workflow/homo.py +++ b/examples/dataset_table_workflow/homo.py @@ -107,7 +107,7 @@ class HomogeneousDatasetWorkflow(TableWorkflow): def labeled_homo_table_example(self): logger.info("Total Item: %d" % (len(self.market))) - methods = ["user_model", "homo_single_aug", "homo_multiple_avg", "homo_ensemble_pruning"] + methods = ["user_model", "homo_single_aug", "homo_ensemble_pruning"] methods_to_retest = [] recorders = {method: Recorder() for method in methods} @@ -147,8 +147,6 @@ class HomogeneousDatasetWorkflow(TableWorkflow): method_configs = { "user_model": {"dataset": self.benchmark.name, "model_type": "lgb"}, "homo_single_aug": {"single_learnware": [single_result[0].learnware]}, - "homo_multiple_aug": common_config, - "homo_multiple_avg": common_config, "homo_ensemble_pruning": common_config } @@ -162,5 +160,5 @@ class HomogeneousDatasetWorkflow(TableWorkflow): for method, recorder in recorders.items(): recorder.save(os.path.join(self.curves_result_path, f"{user}/{user}_{method}_performance.json")) - methods_to_plot = ["user_model", "homo_single_aug", "homo_multiple_avg", "homo_ensemble_pruning"] + methods_to_plot = ["user_model", "homo_single_aug", "homo_ensemble_pruning"] plot_performance_curves(self.curves_result_path, user, {method: recorders[method] for method in methods_to_plot}, task="Homo", n_labeled_list=homo_n_labeled_list) \ No newline at end of file From df2aaf0351ac9f55815f024e9593734e75aecece Mon Sep 17 00:00:00 2001 From: liuht Date: Sat, 13 Jan 2024 00:41:23 +0800 Subject: [PATCH 18/32] [MNT] update hetero_single_aug --- examples/dataset_table_workflow/base.py | 70 ++------------------- examples/dataset_table_workflow/hetero.py | 12 +--- examples/dataset_table_workflow/utils.py | 26 +------- examples/dataset_table_workflow/workflow.py | 4 +- 4 files changed, 10 insertions(+), 102 deletions(-) diff --git a/examples/dataset_table_workflow/base.py b/examples/dataset_table_workflow/base.py index 93a66cd..1634e97 100644 --- a/examples/dataset_table_workflow/base.py +++ b/examples/dataset_table_workflow/base.py @@ -6,27 +6,17 @@ import requests import tempfile import traceback import numpy as np -from queue import Empty -from tqdm import tqdm from learnware.client import LearnwareClient from learnware.logger import get_module_logger from learnware.market import instantiate_learnware_market from learnware.reuse.utils import fill_data_with_mean from learnware.tests.benchmarks import LearnwareBenchmark -from torch.multiprocessing import Process, Queue, set_start_method from config import * from methods import * -from utils import process_single_aug logger = get_module_logger("base_table", level="INFO") -try: - set_start_method('spawn') -except RuntimeError: - pass -torch.multiprocessing.set_sharing_strategy("file_system") - class TableWorkflow: def __init__(self, benchmark_config, name="easy", rebuild=False, retrain=False): @@ -99,16 +89,6 @@ class TableWorkflow: logger.info(f"An error occurred when downloading {learnware_id}: {e}\n{traceback.format_exc()}, retrying...") time.sleep(1) continue - - @staticmethod - def process_learnware_chunk(cuda_idx, method, test_info, loss_func, learnware_chunk, queue): - torch.cuda.set_device(cuda_idx) - for learnware in learnware_chunk: - learnware_index = test_info['learnwares'].index(learnware) - test_info['single_learnware'] = learnware - scores = TableWorkflow._limited_data(method, test_info, loss_func) - torch.cuda.empty_cache() - queue.put((learnware_index, scores)) def test_method(self, test_info, recorders, loss_func=loss_func_rmse): method_name_full = test_info["method_name"] @@ -121,51 +101,9 @@ class TableWorkflow: os.makedirs(save_root_path, exist_ok=True) save_path = os.path.join(save_root_path, f"{method_name}.json") - if method_name_full == "hetero_single_aug": - if recorder.should_test_method(user, idx, save_path): - # * single-process - # bar = tqdm(total=len(test_info["learnwares"]), desc=f"Test {method_name}") - # for learnware in test_info['learnwares']: - # test_info['single_learnware'] = learnware - # scores = self._limited_data(test_methods[method_name_full], test_info, loss_func) - # recorder.record(user, idx, scores) - # bar.update(1) - - # * multi-process - queue = Queue() - processes = [] - bar = tqdm(total=len(test_info["learnwares"]), desc=f"Test {method_name}", unit="learnware") - learnware_chunks = [test_info["learnwares"][i:len(test_info["learnwares"]):len(self.cuda_idx)] for i in self.cuda_idx] - - for cuda_idx, learnware_chunk in zip(self.cuda_idx, learnware_chunks): - p = Process(target=TableWorkflow.process_learnware_chunk, args=(cuda_idx, method, test_info, loss_func, learnware_chunk, queue)) - processes.append(p) - p.start() - - all_results = [] - while any(p.is_alive() for p in processes) or not queue.empty(): - try: - result = queue.get(timeout=0.1) - all_results.append(result) - bar.update(1) - except Empty: - time.sleep(0.1) - continue - bar.close() - - for p in processes: - p.join() - - all_results.sort(key=lambda x: x[0]) - all_scores = [result[1] for result in all_results] - recorder.record(user, all_scores) - recorder.save(save_path) - - process_single_aug(user, idx, recorder.data[user][idx], recorders, save_root_path) - else: - if recorder.should_test_method(user, idx, save_path): - scores = self._limited_data(method, test_info, loss_func) - recorder.record(user, scores) - recorder.save(save_path) + if recorder.should_test_method(user, idx, save_path): + scores = self._limited_data(method, test_info, loss_func) + recorder.record(user, scores) + recorder.save(save_path) logger.info(f"Method {method_name} on {user}_{idx} finished") \ No newline at end of file diff --git a/examples/dataset_table_workflow/hetero.py b/examples/dataset_table_workflow/hetero.py index 21b87e9..b845b3b 100644 --- a/examples/dataset_table_workflow/hetero.py +++ b/examples/dataset_table_workflow/hetero.py @@ -109,7 +109,7 @@ class HeterogeneousDatasetWorkflow(TableWorkflow): def labeled_hetero_table_example(self): logger.info("Total Items: %d" % len(self.market)) methods = ["user_model", "hetero_single_aug", "hetero_multiple_avg", "hetero_ensemble_pruning"] - recorders = {method: Recorder() for method in methods + ["select_score", "oracle_score", "mean_score"]} + recorders = {method: Recorder() for method in methods} user = self.benchmark.name for idx in range(self.benchmark.user_num): @@ -134,10 +134,6 @@ class HeterogeneousDatasetWorkflow(TableWorkflow): search_result = self.market.search_learnware(user_info) single_result = search_result.get_single_results() multiple_result = search_result.get_multiple_results() - - rank_map = {item.learnware.id: index for index, item in enumerate(single_result)} - all_learnwares = self.market.get_learnwares() - all_learnwares.sort(key=lambda learnware: rank_map.get(learnware.id, float('inf'))) if len(multiple_result) > 0: mixture_id = " ".join([learnware.id for learnware in multiple_result[0].learnwares]) @@ -152,8 +148,7 @@ class HeterogeneousDatasetWorkflow(TableWorkflow): common_config = {"user_rkme": user_stat_spec, "learnwares": mixture_learnware_list} method_configs = { "user_model": {"dataset": self.benchmark.name, "model_type": "lgb"}, - "hetero_single_aug": {"user_rkme": user_stat_spec, "learnwares": all_learnwares}, - "hetero_multiple_aug": common_config, + "hetero_single_aug": {"user_rkme": user_stat_spec, "single_learnware": single_result[0].learnware}, "hetero_multiple_avg": common_config, "hetero_ensemble_pruning": common_config } @@ -167,5 +162,4 @@ class HeterogeneousDatasetWorkflow(TableWorkflow): for method, recorder in recorders.items(): recorder.save(os.path.join(self.curves_result_path, f"{user}/{user}_{method}_performance.json")) - methods_to_plot = ["user_model", "select_score", "mean_score", "hetero_multiple_avg", "hetero_ensemble_pruning"] - plot_performance_curves(self.curves_result_path, user, {method: recorders[method] for method in methods_to_plot}, task="Hetero", n_labeled_list=hetero_n_labeled_list) \ No newline at end of file + plot_performance_curves(self.curves_result_path, user, recorders, task="Hetero", n_labeled_list=hetero_n_labeled_list) \ No newline at end of file diff --git a/examples/dataset_table_workflow/utils.py b/examples/dataset_table_workflow/utils.py index 1e23e8e..730ef3a 100644 --- a/examples/dataset_table_workflow/utils.py +++ b/examples/dataset_table_workflow/utils.py @@ -37,31 +37,7 @@ class Recorder: self.load(path) return user not in self.data or idx > len(self.data[user]) - 1 return True - - -def process_single_aug(user, idx, scores, recorders, root_path): - try: - n_labeled = len(scores[0]) - select_scores, mean_scores, oracle_scores = [], [], [] - for i in range(n_labeled): - sub_scores_array = np.vstack([lst[i] for lst in scores]) - sub_scores_select = np.squeeze(sub_scores_array[0]) - sub_scores_mean = np.squeeze(np.mean(sub_scores_array, axis=0)) - sub_scores_min = np.squeeze(np.min(sub_scores_array, axis=0)) - - select_scores.append(sub_scores_select.tolist()) - mean_scores.append(sub_scores_mean.tolist()) - oracle_scores.append(sub_scores_min.tolist()) - - for method_name, scores in zip(["select_score", "mean_score", "oracle_score"], - [select_scores, mean_scores, oracle_scores]): - recorders[method_name].record(user, scores) - save_path = os.path.join(root_path, f"{method_name}.json") - recorders[method_name].save(save_path) - except Exception: - error_message = traceback.format_exc() - logger.error(f"Error in process_single_aug for user {user}, idx {idx}: {error_message}") - + def plot_performance_curves(path, user, recorders, task, n_labeled_list): plt.figure(figsize=(10, 6)) diff --git a/examples/dataset_table_workflow/workflow.py b/examples/dataset_table_workflow/workflow.py index 5dbe80d..73ebc86 100644 --- a/examples/dataset_table_workflow/workflow.py +++ b/examples/dataset_table_workflow/workflow.py @@ -38,8 +38,8 @@ class TableDatasetWorkflow: workflow = HeterogeneousDatasetWorkflow( benchmark_config=hetero_cross_task_benchmark_config, name="hetero", - rebuild=False, - retrain=False + rebuild=True, + retrain=True ) workflow.labeled_hetero_table_example() From 9ac319b5ad73fa6b16fdf729f33d25805912671a Mon Sep 17 00:00:00 2001 From: liuht Date: Sat, 13 Jan 2024 01:28:15 +0800 Subject: [PATCH 19/32] [MNT] update hetero_single_aug --- examples/dataset_table_workflow/base.py | 70 ++------------------- examples/dataset_table_workflow/hetero.py | 12 +--- examples/dataset_table_workflow/utils.py | 31 +-------- examples/dataset_table_workflow/workflow.py | 4 +- 4 files changed, 11 insertions(+), 106 deletions(-) diff --git a/examples/dataset_table_workflow/base.py b/examples/dataset_table_workflow/base.py index 93a66cd..1634e97 100644 --- a/examples/dataset_table_workflow/base.py +++ b/examples/dataset_table_workflow/base.py @@ -6,27 +6,17 @@ import requests import tempfile import traceback import numpy as np -from queue import Empty -from tqdm import tqdm from learnware.client import LearnwareClient from learnware.logger import get_module_logger from learnware.market import instantiate_learnware_market from learnware.reuse.utils import fill_data_with_mean from learnware.tests.benchmarks import LearnwareBenchmark -from torch.multiprocessing import Process, Queue, set_start_method from config import * from methods import * -from utils import process_single_aug logger = get_module_logger("base_table", level="INFO") -try: - set_start_method('spawn') -except RuntimeError: - pass -torch.multiprocessing.set_sharing_strategy("file_system") - class TableWorkflow: def __init__(self, benchmark_config, name="easy", rebuild=False, retrain=False): @@ -99,16 +89,6 @@ class TableWorkflow: logger.info(f"An error occurred when downloading {learnware_id}: {e}\n{traceback.format_exc()}, retrying...") time.sleep(1) continue - - @staticmethod - def process_learnware_chunk(cuda_idx, method, test_info, loss_func, learnware_chunk, queue): - torch.cuda.set_device(cuda_idx) - for learnware in learnware_chunk: - learnware_index = test_info['learnwares'].index(learnware) - test_info['single_learnware'] = learnware - scores = TableWorkflow._limited_data(method, test_info, loss_func) - torch.cuda.empty_cache() - queue.put((learnware_index, scores)) def test_method(self, test_info, recorders, loss_func=loss_func_rmse): method_name_full = test_info["method_name"] @@ -121,51 +101,9 @@ class TableWorkflow: os.makedirs(save_root_path, exist_ok=True) save_path = os.path.join(save_root_path, f"{method_name}.json") - if method_name_full == "hetero_single_aug": - if recorder.should_test_method(user, idx, save_path): - # * single-process - # bar = tqdm(total=len(test_info["learnwares"]), desc=f"Test {method_name}") - # for learnware in test_info['learnwares']: - # test_info['single_learnware'] = learnware - # scores = self._limited_data(test_methods[method_name_full], test_info, loss_func) - # recorder.record(user, idx, scores) - # bar.update(1) - - # * multi-process - queue = Queue() - processes = [] - bar = tqdm(total=len(test_info["learnwares"]), desc=f"Test {method_name}", unit="learnware") - learnware_chunks = [test_info["learnwares"][i:len(test_info["learnwares"]):len(self.cuda_idx)] for i in self.cuda_idx] - - for cuda_idx, learnware_chunk in zip(self.cuda_idx, learnware_chunks): - p = Process(target=TableWorkflow.process_learnware_chunk, args=(cuda_idx, method, test_info, loss_func, learnware_chunk, queue)) - processes.append(p) - p.start() - - all_results = [] - while any(p.is_alive() for p in processes) or not queue.empty(): - try: - result = queue.get(timeout=0.1) - all_results.append(result) - bar.update(1) - except Empty: - time.sleep(0.1) - continue - bar.close() - - for p in processes: - p.join() - - all_results.sort(key=lambda x: x[0]) - all_scores = [result[1] for result in all_results] - recorder.record(user, all_scores) - recorder.save(save_path) - - process_single_aug(user, idx, recorder.data[user][idx], recorders, save_root_path) - else: - if recorder.should_test_method(user, idx, save_path): - scores = self._limited_data(method, test_info, loss_func) - recorder.record(user, scores) - recorder.save(save_path) + if recorder.should_test_method(user, idx, save_path): + scores = self._limited_data(method, test_info, loss_func) + recorder.record(user, scores) + recorder.save(save_path) logger.info(f"Method {method_name} on {user}_{idx} finished") \ No newline at end of file diff --git a/examples/dataset_table_workflow/hetero.py b/examples/dataset_table_workflow/hetero.py index 21b87e9..b845b3b 100644 --- a/examples/dataset_table_workflow/hetero.py +++ b/examples/dataset_table_workflow/hetero.py @@ -109,7 +109,7 @@ class HeterogeneousDatasetWorkflow(TableWorkflow): def labeled_hetero_table_example(self): logger.info("Total Items: %d" % len(self.market)) methods = ["user_model", "hetero_single_aug", "hetero_multiple_avg", "hetero_ensemble_pruning"] - recorders = {method: Recorder() for method in methods + ["select_score", "oracle_score", "mean_score"]} + recorders = {method: Recorder() for method in methods} user = self.benchmark.name for idx in range(self.benchmark.user_num): @@ -134,10 +134,6 @@ class HeterogeneousDatasetWorkflow(TableWorkflow): search_result = self.market.search_learnware(user_info) single_result = search_result.get_single_results() multiple_result = search_result.get_multiple_results() - - rank_map = {item.learnware.id: index for index, item in enumerate(single_result)} - all_learnwares = self.market.get_learnwares() - all_learnwares.sort(key=lambda learnware: rank_map.get(learnware.id, float('inf'))) if len(multiple_result) > 0: mixture_id = " ".join([learnware.id for learnware in multiple_result[0].learnwares]) @@ -152,8 +148,7 @@ class HeterogeneousDatasetWorkflow(TableWorkflow): common_config = {"user_rkme": user_stat_spec, "learnwares": mixture_learnware_list} method_configs = { "user_model": {"dataset": self.benchmark.name, "model_type": "lgb"}, - "hetero_single_aug": {"user_rkme": user_stat_spec, "learnwares": all_learnwares}, - "hetero_multiple_aug": common_config, + "hetero_single_aug": {"user_rkme": user_stat_spec, "single_learnware": single_result[0].learnware}, "hetero_multiple_avg": common_config, "hetero_ensemble_pruning": common_config } @@ -167,5 +162,4 @@ class HeterogeneousDatasetWorkflow(TableWorkflow): for method, recorder in recorders.items(): recorder.save(os.path.join(self.curves_result_path, f"{user}/{user}_{method}_performance.json")) - methods_to_plot = ["user_model", "select_score", "mean_score", "hetero_multiple_avg", "hetero_ensemble_pruning"] - plot_performance_curves(self.curves_result_path, user, {method: recorders[method] for method in methods_to_plot}, task="Hetero", n_labeled_list=hetero_n_labeled_list) \ No newline at end of file + plot_performance_curves(self.curves_result_path, user, recorders, task="Hetero", n_labeled_list=hetero_n_labeled_list) \ No newline at end of file diff --git a/examples/dataset_table_workflow/utils.py b/examples/dataset_table_workflow/utils.py index 1e23e8e..702126f 100644 --- a/examples/dataset_table_workflow/utils.py +++ b/examples/dataset_table_workflow/utils.py @@ -37,39 +37,12 @@ class Recorder: self.load(path) return user not in self.data or idx > len(self.data[user]) - 1 return True - - -def process_single_aug(user, idx, scores, recorders, root_path): - try: - n_labeled = len(scores[0]) - select_scores, mean_scores, oracle_scores = [], [], [] - for i in range(n_labeled): - sub_scores_array = np.vstack([lst[i] for lst in scores]) - sub_scores_select = np.squeeze(sub_scores_array[0]) - sub_scores_mean = np.squeeze(np.mean(sub_scores_array, axis=0)) - sub_scores_min = np.squeeze(np.min(sub_scores_array, axis=0)) - - select_scores.append(sub_scores_select.tolist()) - mean_scores.append(sub_scores_mean.tolist()) - oracle_scores.append(sub_scores_min.tolist()) - - for method_name, scores in zip(["select_score", "mean_score", "oracle_score"], - [select_scores, mean_scores, oracle_scores]): - recorders[method_name].record(user, scores) - save_path = os.path.join(root_path, f"{method_name}.json") - recorders[method_name].save(save_path) - except Exception: - error_message = traceback.format_exc() - logger.error(f"Error in process_single_aug for user {user}, idx {idx}: {error_message}") - + def plot_performance_curves(path, user, recorders, task, n_labeled_list): plt.figure(figsize=(10, 6)) plt.xticks(range(len(n_labeled_list)), n_labeled_list) - for method, recorder in recorders.items(): - if method == "hetero_single_aug": - continue - + for method, recorder in recorders.items(): data_path = os.path.join(path, f"{user}/{user}_{method}_performance.json") recorder.load(data_path) scores_array = recorder.get_performance_data(user) diff --git a/examples/dataset_table_workflow/workflow.py b/examples/dataset_table_workflow/workflow.py index 5dbe80d..73ebc86 100644 --- a/examples/dataset_table_workflow/workflow.py +++ b/examples/dataset_table_workflow/workflow.py @@ -38,8 +38,8 @@ class TableDatasetWorkflow: workflow = HeterogeneousDatasetWorkflow( benchmark_config=hetero_cross_task_benchmark_config, name="hetero", - rebuild=False, - retrain=False + rebuild=True, + retrain=True ) workflow.labeled_hetero_table_example() From ebf482b84d3261e6e5314348250e667297bcfda2 Mon Sep 17 00:00:00 2001 From: liuht Date: Sat, 13 Jan 2024 09:30:48 +0800 Subject: [PATCH 20/32] [FIX] delete unused lgb params --- examples/dataset_table_workflow/config.py | 17 ----------------- examples/dataset_table_workflow/workflow.py | 4 ++-- 2 files changed, 2 insertions(+), 19 deletions(-) diff --git a/examples/dataset_table_workflow/config.py b/examples/dataset_table_workflow/config.py index a23cc31..7f0d5de 100644 --- a/examples/dataset_table_workflow/config.py +++ b/examples/dataset_table_workflow/config.py @@ -61,23 +61,6 @@ market_mapping_params = { } user_model_params = { - # "Corporacion": { - # "lgb": { - # "params": { - # "num_leaves": 31, - # "objective": "regression", - # "learning_rate": 0.1, - # "feature_fraction": 0.8, - # "bagging_fraction": 0.8, - # "bagging_freq": 2, - # "metric": "l2", - # "num_threads": 4, - # "verbose": -1, - # }, - # "MAX_ROUNDS": 500, - # "early_stopping_rounds": 50, - # } - # }, "M5": { "lgb": { "params": { diff --git a/examples/dataset_table_workflow/workflow.py b/examples/dataset_table_workflow/workflow.py index 73ebc86..5dbe80d 100644 --- a/examples/dataset_table_workflow/workflow.py +++ b/examples/dataset_table_workflow/workflow.py @@ -38,8 +38,8 @@ class TableDatasetWorkflow: workflow = HeterogeneousDatasetWorkflow( benchmark_config=hetero_cross_task_benchmark_config, name="hetero", - rebuild=True, - retrain=True + rebuild=False, + retrain=False ) workflow.labeled_hetero_table_example() From 703c158eb3dc94725bcb20917ff3eb39450ceb8e Mon Sep 17 00:00:00 2001 From: liuht Date: Sat, 13 Jan 2024 17:19:30 +0800 Subject: [PATCH 21/32] [FIX] reproduce market engine --- examples/dataset_table_workflow/base.py | 16 ++++++++- examples/dataset_table_workflow/config.py | 2 +- examples/dataset_table_workflow/hetero.py | 38 ++++++++++----------- examples/dataset_table_workflow/utils.py | 17 +++++++-- examples/dataset_table_workflow/workflow.py | 4 +-- 5 files changed, 52 insertions(+), 25 deletions(-) diff --git a/examples/dataset_table_workflow/base.py b/examples/dataset_table_workflow/base.py index 1634e97..0381f28 100644 --- a/examples/dataset_table_workflow/base.py +++ b/examples/dataset_table_workflow/base.py @@ -14,10 +14,14 @@ from learnware.tests.benchmarks import LearnwareBenchmark from config import * from methods import * +from utils import set_seed logger = get_module_logger("base_table", level="INFO") +# for quick test only +from learnware.market.heterogeneous import HeteroMapTableOrganizer + class TableWorkflow: def __init__(self, benchmark_config, name="easy", rebuild=False, retrain=False): self.root_path = os.path.abspath(os.path.join(__file__, "..")) @@ -25,9 +29,10 @@ class TableWorkflow: self.curves_result_path = os.path.join(self.result_path, "curves") os.makedirs(self.result_path, exist_ok=True) os.makedirs(self.curves_result_path, exist_ok=True) + # if name == "hetero": + # set_seed(42) self._prepare_market(benchmark_config, name, rebuild, retrain) - self.cuda_idx = list(range(torch.cuda.device_count())) @staticmethod def _limited_data(method, test_info, loss_func): @@ -75,6 +80,15 @@ class TableWorkflow: ) self.user_semantic = client.get_semantic_specification(self.benchmark.learnware_ids[0]) self.user_semantic["Name"]["Values"] = "" + + # if retrain == True and rebuild == False: + # logger.info(f"training learnwares: {self.market.get_learnware_ids()[::-1]}") + # market_mapping = HeteroMapTableOrganizer.train(self.market.get_learnwares()[::-1], save_dir='test_model.bin', **market_mapping_params) + # self.market.learnware_organizer.market_mapping = market_mapping + # self.market.learnware_organizer._update_learnware_hetero_spec(self.market.get_learnware_ids()[::-1]) + + # return + if len(self.market) == 0 or rebuild == True: for learnware_id in self.benchmark.learnware_ids: with tempfile.TemporaryDirectory(prefix="table_benchmark_") as tempdir: diff --git a/examples/dataset_table_workflow/config.py b/examples/dataset_table_workflow/config.py index 7f0d5de..5b6a74f 100644 --- a/examples/dataset_table_workflow/config.py +++ b/examples/dataset_table_workflow/config.py @@ -216,7 +216,7 @@ hetero_cross_feat_eng_benchmark_config = BenchmarkConfig( "00000912" ], test_data_path="PFS/test_data.zip", - train_data_path="PFS/train_data.zip", + # train_data_path="PFS/train_data.zip", extra_info_path="PFS/extra_info.zip" ) diff --git a/examples/dataset_table_workflow/hetero.py b/examples/dataset_table_workflow/hetero.py index b845b3b..db1c1a7 100644 --- a/examples/dataset_table_workflow/hetero.py +++ b/examples/dataset_table_workflow/hetero.py @@ -43,7 +43,7 @@ class HeterogeneousDatasetWorkflow(TableWorkflow): single_result = search_result.get_single_results() multiple_result = search_result.get_multiple_results() - logger.info(f"hetero search result of user {user}_{idx}:") + logger.info(f"hetero search result of user {user}_{idx}: {single_result[0].learnware.id}") logger.info( f"single model num: {len(single_result)}, max_score: {single_result[0].score}, min_score: {single_result[-1].score}" ) @@ -53,15 +53,15 @@ class HeterogeneousDatasetWorkflow(TableWorkflow): pred_y = single_hetero_learnware.predict(test_x) single_score_list.append(loss_func_rmse(pred_y, test_y)) - rmse_list = [] - for learnware in all_learnwares: - hetero_learnware = FeatureAlignLearnware(learnware, **align_model_params) - hetero_learnware.align(user_rkme=user_stat_spec) - pred_y = hetero_learnware.predict(test_x) - rmse_list.append(loss_func_rmse(pred_y, test_y)) - logger.info( - f"Top1-score: {single_result[0].score}, learnware_id: {single_result[0].learnware.id}, rmse: {single_score_list[0]}" - ) + # rmse_list = [] + # for learnware in all_learnwares: + # hetero_learnware = FeatureAlignLearnware(learnware, **align_model_params) + # hetero_learnware.align(user_rkme=user_stat_spec) + # pred_y = hetero_learnware.predict(test_x) + # rmse_list.append(loss_func_rmse(pred_y, test_y)) + # logger.info( + # f"Top1-score: {single_result[0].score}, learnware_id: {single_result[0].learnware.id}, rmse: {single_score_list[0]}" + # ) if len(multiple_result) > 0: mixture_id = " ".join([learnware.id for learnware in multiple_result[0].learnwares]) @@ -83,21 +83,21 @@ class HeterogeneousDatasetWorkflow(TableWorkflow): ensemble_score_list.append(ensemble_score) logger.info(f"mixture reuse rmse (ensemble): {ensemble_score}") - learnware_rmse_list.append(rmse_list) + # learnware_rmse_list.append(rmse_list) - single_list = np.array(learnware_rmse_list) - avg_score_list = [np.mean(lst, axis=0) for lst in single_list] - oracle_score_list = [np.min(lst, axis=0) for lst in single_list] + # single_list = np.array(learnware_rmse_list) + # avg_score_list = [np.mean(lst, axis=0) for lst in single_list] + # oracle_score_list = [np.min(lst, axis=0) for lst in single_list] logger.info( - "RMSE of selected learnware: %.3f +/- %.3f, Average performance: %.3f +/- %.3f, Oracle performace: %.3f +/- %.3f" + "RMSE of selected learnware: %.3f +/- %.3f" # , Average performance: %.3f +/- %.3f, Oracle performace: %.3f +/- %.3f" % ( np.mean(single_score_list), np.std(single_score_list), - np.mean(avg_score_list), - np.std(avg_score_list), - np.mean(oracle_score_list), - np.std(oracle_score_list), + # np.mean(avg_score_list), + # np.std(avg_score_list), + # np.mean(oracle_score_list), + # np.std(oracle_score_list), ) ) logger.info( diff --git a/examples/dataset_table_workflow/utils.py b/examples/dataset_table_workflow/utils.py index 702126f..f176a6a 100644 --- a/examples/dataset_table_workflow/utils.py +++ b/examples/dataset_table_workflow/utils.py @@ -1,6 +1,8 @@ import os import json -import traceback +import random + +import torch import numpy as np import matplotlib.pyplot as plt from collections import defaultdict @@ -78,4 +80,15 @@ def plot_performance_curves(path, user, recorders, task, n_labeled_list): root_path = os.path.abspath(os.path.join(__file__, "..")) fig_path = os.path.join(root_path, "results", "figs") os.makedirs(fig_path, exist_ok=True) - plt.savefig(os.path.join(fig_path, f"{task}_labeled_curves.svg"), bbox_inches="tight", dpi=700) \ No newline at end of file + plt.savefig(os.path.join(fig_path, f"{task}_labeled_curves.svg"), bbox_inches="tight", dpi=700) + + +def set_seed(seed): + random.seed(seed) + os.environ["PYTHONHASHSEED"] = str(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + torch.backends.cudnn.benchmark = False + torch.backends.cudnn.deterministic = True \ No newline at end of file diff --git a/examples/dataset_table_workflow/workflow.py b/examples/dataset_table_workflow/workflow.py index 5dbe80d..e03dca3 100644 --- a/examples/dataset_table_workflow/workflow.py +++ b/examples/dataset_table_workflow/workflow.py @@ -29,8 +29,8 @@ class TableDatasetWorkflow: workflow = HeterogeneousDatasetWorkflow( benchmark_config=hetero_cross_feat_eng_benchmark_config, name="hetero", - rebuild=False, - retrain=False + rebuild=True, + retrain=True ) workflow.unlabeled_hetero_table_example() From 3d61d993d49feddb204add14bf34fe282d2417dc Mon Sep 17 00:00:00 2001 From: liuht Date: Sat, 13 Jan 2024 21:48:38 +0800 Subject: [PATCH 22/32] [MNT] set default values --- examples/dataset_table_workflow/base.py | 16 +-------- examples/dataset_table_workflow/config.py | 2 +- examples/dataset_table_workflow/hetero.py | 38 ++++++++++----------- examples/dataset_table_workflow/workflow.py | 7 ++-- 4 files changed, 26 insertions(+), 37 deletions(-) diff --git a/examples/dataset_table_workflow/base.py b/examples/dataset_table_workflow/base.py index 0381f28..571f4fa 100644 --- a/examples/dataset_table_workflow/base.py +++ b/examples/dataset_table_workflow/base.py @@ -19,9 +19,6 @@ from utils import set_seed logger = get_module_logger("base_table", level="INFO") -# for quick test only -from learnware.market.heterogeneous import HeteroMapTableOrganizer - class TableWorkflow: def __init__(self, benchmark_config, name="easy", rebuild=False, retrain=False): self.root_path = os.path.abspath(os.path.join(__file__, "..")) @@ -29,10 +26,7 @@ class TableWorkflow: self.curves_result_path = os.path.join(self.result_path, "curves") os.makedirs(self.result_path, exist_ok=True) os.makedirs(self.curves_result_path, exist_ok=True) - # if name == "hetero": - # set_seed(42) - self._prepare_market(benchmark_config, name, rebuild, retrain) - + self._prepare_market(benchmark_config, name, rebuild, retrain) @staticmethod def _limited_data(method, test_info, loss_func): @@ -81,14 +75,6 @@ class TableWorkflow: self.user_semantic = client.get_semantic_specification(self.benchmark.learnware_ids[0]) self.user_semantic["Name"]["Values"] = "" - # if retrain == True and rebuild == False: - # logger.info(f"training learnwares: {self.market.get_learnware_ids()[::-1]}") - # market_mapping = HeteroMapTableOrganizer.train(self.market.get_learnwares()[::-1], save_dir='test_model.bin', **market_mapping_params) - # self.market.learnware_organizer.market_mapping = market_mapping - # self.market.learnware_organizer._update_learnware_hetero_spec(self.market.get_learnware_ids()[::-1]) - - # return - if len(self.market) == 0 or rebuild == True: for learnware_id in self.benchmark.learnware_ids: with tempfile.TemporaryDirectory(prefix="table_benchmark_") as tempdir: diff --git a/examples/dataset_table_workflow/config.py b/examples/dataset_table_workflow/config.py index 5b6a74f..7f0d5de 100644 --- a/examples/dataset_table_workflow/config.py +++ b/examples/dataset_table_workflow/config.py @@ -216,7 +216,7 @@ hetero_cross_feat_eng_benchmark_config = BenchmarkConfig( "00000912" ], test_data_path="PFS/test_data.zip", - # train_data_path="PFS/train_data.zip", + train_data_path="PFS/train_data.zip", extra_info_path="PFS/extra_info.zip" ) diff --git a/examples/dataset_table_workflow/hetero.py b/examples/dataset_table_workflow/hetero.py index db1c1a7..3caa5d3 100644 --- a/examples/dataset_table_workflow/hetero.py +++ b/examples/dataset_table_workflow/hetero.py @@ -39,7 +39,7 @@ class HeterogeneousDatasetWorkflow(TableWorkflow): ) logger.info(f"Searching Market for user: {user}_{idx}") - search_result = self.market.search_learnware(user_info) + search_result = self.market.search_learnware(user_info, max_search_num=10) single_result = search_result.get_single_results() multiple_result = search_result.get_multiple_results() @@ -53,15 +53,15 @@ class HeterogeneousDatasetWorkflow(TableWorkflow): pred_y = single_hetero_learnware.predict(test_x) single_score_list.append(loss_func_rmse(pred_y, test_y)) - # rmse_list = [] - # for learnware in all_learnwares: - # hetero_learnware = FeatureAlignLearnware(learnware, **align_model_params) - # hetero_learnware.align(user_rkme=user_stat_spec) - # pred_y = hetero_learnware.predict(test_x) - # rmse_list.append(loss_func_rmse(pred_y, test_y)) - # logger.info( - # f"Top1-score: {single_result[0].score}, learnware_id: {single_result[0].learnware.id}, rmse: {single_score_list[0]}" - # ) + rmse_list = [] + for learnware in all_learnwares: + hetero_learnware = FeatureAlignLearnware(learnware, **align_model_params) + hetero_learnware.align(user_rkme=user_stat_spec) + pred_y = hetero_learnware.predict(test_x) + rmse_list.append(loss_func_rmse(pred_y, test_y)) + logger.info( + f"Top1-score: {single_result[0].score}, learnware_id: {single_result[0].learnware.id}, rmse: {single_score_list[0]}" + ) if len(multiple_result) > 0: mixture_id = " ".join([learnware.id for learnware in multiple_result[0].learnwares]) @@ -83,21 +83,21 @@ class HeterogeneousDatasetWorkflow(TableWorkflow): ensemble_score_list.append(ensemble_score) logger.info(f"mixture reuse rmse (ensemble): {ensemble_score}") - # learnware_rmse_list.append(rmse_list) + learnware_rmse_list.append(rmse_list) - # single_list = np.array(learnware_rmse_list) - # avg_score_list = [np.mean(lst, axis=0) for lst in single_list] - # oracle_score_list = [np.min(lst, axis=0) for lst in single_list] + single_list = np.array(learnware_rmse_list) + avg_score_list = [np.mean(lst, axis=0) for lst in single_list] + oracle_score_list = [np.min(lst, axis=0) for lst in single_list] logger.info( - "RMSE of selected learnware: %.3f +/- %.3f" # , Average performance: %.3f +/- %.3f, Oracle performace: %.3f +/- %.3f" + "RMSE of selected learnware: %.3f +/- %.3f, Average performance: %.3f +/- %.3f, Oracle performace: %.3f +/- %.3f" % ( np.mean(single_score_list), np.std(single_score_list), - # np.mean(avg_score_list), - # np.std(avg_score_list), - # np.mean(oracle_score_list), - # np.std(oracle_score_list), + np.mean(avg_score_list), + np.std(avg_score_list), + np.mean(oracle_score_list), + np.std(oracle_score_list) ) ) logger.info( diff --git a/examples/dataset_table_workflow/workflow.py b/examples/dataset_table_workflow/workflow.py index e03dca3..e079b6e 100644 --- a/examples/dataset_table_workflow/workflow.py +++ b/examples/dataset_table_workflow/workflow.py @@ -4,6 +4,7 @@ from learnware.logger import get_module_logger from homo import HomogeneousDatasetWorkflow from hetero import HeterogeneousDatasetWorkflow from config import homo_table_benchmark_config, hetero_cross_feat_eng_benchmark_config, hetero_cross_task_benchmark_config +from utils import set_seed logger = get_module_logger("base_table", level="INFO") @@ -26,15 +27,17 @@ class TableDatasetWorkflow: workflow.labeled_homo_table_example() def cross_feat_eng_hetero_table_example(self): + set_seed(0) workflow = HeterogeneousDatasetWorkflow( benchmark_config=hetero_cross_feat_eng_benchmark_config, name="hetero", - rebuild=True, - retrain=True + rebuild=False, + retrain=False ) workflow.unlabeled_hetero_table_example() def cross_task_hetero_table_example(self): + set_seed(0) workflow = HeterogeneousDatasetWorkflow( benchmark_config=hetero_cross_task_benchmark_config, name="hetero", From 80e58a77f869551fd0e82a476de84a3ea36bc236 Mon Sep 17 00:00:00 2001 From: liuht Date: Sun, 14 Jan 2024 18:28:05 +0800 Subject: [PATCH 23/32] [MNT] add skip test --- examples/dataset_table_workflow/hetero.py | 95 ++++++++++---------- examples/dataset_table_workflow/homo.py | 100 +++++++++++----------- 2 files changed, 98 insertions(+), 97 deletions(-) diff --git a/examples/dataset_table_workflow/hetero.py b/examples/dataset_table_workflow/hetero.py index 3caa5d3..a3112f7 100644 --- a/examples/dataset_table_workflow/hetero.py +++ b/examples/dataset_table_workflow/hetero.py @@ -106,60 +106,61 @@ class HeterogeneousDatasetWorkflow(TableWorkflow): ) - def labeled_hetero_table_example(self): + def labeled_hetero_table_example(self, skip_test=False): logger.info("Total Items: %d" % len(self.market)) methods = ["user_model", "hetero_single_aug", "hetero_multiple_avg", "hetero_ensemble_pruning"] recorders = {method: Recorder() for method in methods} - user = self.benchmark.name - for idx in range(self.benchmark.user_num): - test_x, test_y = self.benchmark.get_test_data(user_ids=idx) - test_x, test_y = test_x.values, test_y.values - - train_x, train_y = self.benchmark.get_train_data(user_ids=idx) - train_x, train_y, feature_descriptions = train_x.values, train_y.values, train_x.columns - train_subsets = self.get_train_subsets(hetero_n_labeled_list, hetero_n_repeat_list, train_x, train_y) - - user_stat_spec = generate_stat_spec(type="table", X=test_x) - input_description = { - "Dimension": len(feature_descriptions), - "Description": {str(i): feature_descriptions[i] for i in range(len(feature_descriptions))} - } - user_semantic["Input"] = input_description - user_info = BaseUserInfo( - semantic_spec=user_semantic, stat_info={user_stat_spec.type: user_stat_spec} - ) - logger.info(f"Searching Market for user: {user}_{idx}") + + if not skip_test: + for idx in range(self.benchmark.user_num): + test_x, test_y = self.benchmark.get_test_data(user_ids=idx) + test_x, test_y = test_x.values, test_y.values + + train_x, train_y = self.benchmark.get_train_data(user_ids=idx) + train_x, train_y, feature_descriptions = train_x.values, train_y.values, train_x.columns + train_subsets = self.get_train_subsets(hetero_n_labeled_list, hetero_n_repeat_list, train_x, train_y) + + user_stat_spec = generate_stat_spec(type="table", X=test_x) + input_description = { + "Dimension": len(feature_descriptions), + "Description": {str(i): feature_descriptions[i] for i in range(len(feature_descriptions))} + } + user_semantic["Input"] = input_description + user_info = BaseUserInfo( + semantic_spec=user_semantic, stat_info={user_stat_spec.type: user_stat_spec} + ) + logger.info(f"Searching Market for user: {user}_{idx}") - search_result = self.market.search_learnware(user_info) - single_result = search_result.get_single_results() - multiple_result = search_result.get_multiple_results() + search_result = self.market.search_learnware(user_info) + single_result = search_result.get_single_results() + multiple_result = search_result.get_multiple_results() - if len(multiple_result) > 0: - mixture_id = " ".join([learnware.id for learnware in multiple_result[0].learnwares]) - logger.info(f"Mixture score: {multiple_result[0].score}, Mixture learnware: {mixture_id}") - mixture_learnware_list = multiple_result[0].learnwares - else: - mixture_learnware_list = [single_result[0].learnware] - - logger.info(f"Hetero search result of user {user}_{idx}: mixture learnware num: {len(mixture_learnware_list)}") + if len(multiple_result) > 0: + mixture_id = " ".join([learnware.id for learnware in multiple_result[0].learnwares]) + logger.info(f"Mixture score: {multiple_result[0].score}, Mixture learnware: {mixture_id}") + mixture_learnware_list = multiple_result[0].learnwares + else: + mixture_learnware_list = [single_result[0].learnware] + + logger.info(f"Hetero search result of user {user}_{idx}: mixture learnware num: {len(mixture_learnware_list)}") - test_info = {"user": user, "idx": idx, "train_subsets": train_subsets, "test_x": test_x, "test_y": test_y, "n_labeled_list": hetero_n_labeled_list} - common_config = {"user_rkme": user_stat_spec, "learnwares": mixture_learnware_list} - method_configs = { - "user_model": {"dataset": self.benchmark.name, "model_type": "lgb"}, - "hetero_single_aug": {"user_rkme": user_stat_spec, "single_learnware": single_result[0].learnware}, - "hetero_multiple_avg": common_config, - "hetero_ensemble_pruning": common_config - } + test_info = {"user": user, "idx": idx, "train_subsets": train_subsets, "test_x": test_x, "test_y": test_y, "n_labeled_list": hetero_n_labeled_list} + common_config = {"user_rkme": user_stat_spec, "learnwares": mixture_learnware_list} + method_configs = { + "user_model": {"dataset": self.benchmark.name, "model_type": "lgb"}, + "hetero_single_aug": {"user_rkme": user_stat_spec, "single_learnware": single_result[0].learnware}, + "hetero_multiple_avg": common_config, + "hetero_ensemble_pruning": common_config + } - for method_name in methods: - logger.info(f"Testing method {method_name}") - test_info["method_name"] = method_name - test_info.update(method_configs[method_name]) - self.test_method(test_info, recorders, loss_func=loss_func_rmse) - - for method, recorder in recorders.items(): - recorder.save(os.path.join(self.curves_result_path, f"{user}/{user}_{method}_performance.json")) + for method_name in methods: + logger.info(f"Testing method {method_name}") + test_info["method_name"] = method_name + test_info.update(method_configs[method_name]) + self.test_method(test_info, recorders, loss_func=loss_func_rmse) + + for method, recorder in recorders.items(): + recorder.save(os.path.join(self.curves_result_path, f"{user}/{user}_{method}_performance.json")) plot_performance_curves(self.curves_result_path, user, recorders, task="Hetero", n_labeled_list=hetero_n_labeled_list) \ No newline at end of file diff --git a/examples/dataset_table_workflow/homo.py b/examples/dataset_table_workflow/homo.py index 73c0544..8bb1e41 100644 --- a/examples/dataset_table_workflow/homo.py +++ b/examples/dataset_table_workflow/homo.py @@ -105,60 +105,60 @@ class HomogeneousDatasetWorkflow(TableWorkflow): ) - def labeled_homo_table_example(self): + def labeled_homo_table_example(self, skip_test=True): logger.info("Total Item: %d" % (len(self.market))) methods = ["user_model", "homo_single_aug", "homo_ensemble_pruning"] methods_to_retest = [] recorders = {method: Recorder() for method in methods} - user = self.benchmark.name - for idx in range(self.benchmark.user_num): - test_x, test_y = self.benchmark.get_test_data(user_ids=idx) - test_x, test_y = test_x.values, test_y.values - - train_x, train_y = self.benchmark.get_train_data(user_ids=idx) - train_x, train_y = train_x.values, train_y.values - train_subsets = self.get_train_subsets(homo_n_labeled_list, homo_n_repeat_list, train_x, train_y) - - user_stat_spec = generate_stat_spec(type="table", X=test_x) - user_info = BaseUserInfo( - semantic_spec=self.user_semantic, stat_info={"RKMETableSpecification": user_stat_spec} - ) - logger.info(f"Searching Market for user: {user}_{idx}") - - search_result = self.market.search_learnware(user_info) - single_result = search_result.get_single_results() - multiple_result = search_result.get_multiple_results() - - logger.info(f"search result of user {user}_{idx}:") - logger.info( - f"single model num: {len(single_result)}, max_score: {single_result[0].score}, min_score: {single_result[-1].score}" - ) - - if len(multiple_result) > 0: - mixture_id = " ".join([learnware.id for learnware in multiple_result[0].learnwares]) - logger.info(f"mixture_score: {multiple_result[0].score}, mixture_learnware: {mixture_id}") - mixture_learnware_list = multiple_result[0].learnwares - else: - mixture_learnware_list = [single_result[0].learnware] - - test_info = {"user": user, "idx": idx, "train_subsets": train_subsets, "test_x": test_x, "test_y": test_y} - common_config = {"learnwares": mixture_learnware_list} - method_configs = { - "user_model": {"dataset": self.benchmark.name, "model_type": "lgb"}, - "homo_single_aug": {"single_learnware": [single_result[0].learnware]}, - "homo_ensemble_pruning": common_config - } - - for method_name in methods: - logger.info(f"Testing method {method_name}") - test_info["method_name"] = method_name - test_info["force"] = method_name in methods_to_retest - test_info.update(method_configs[method_name]) - self.test_method(test_info, recorders, loss_func=loss_func_rmse) - for method, recorder in recorders.items(): - recorder.save(os.path.join(self.curves_result_path, f"{user}/{user}_{method}_performance.json")) + if not skip_test: + for idx in range(self.benchmark.user_num): + test_x, test_y = self.benchmark.get_test_data(user_ids=idx) + test_x, test_y = test_x.values, test_y.values + + train_x, train_y = self.benchmark.get_train_data(user_ids=idx) + train_x, train_y = train_x.values, train_y.values + train_subsets = self.get_train_subsets(homo_n_labeled_list, homo_n_repeat_list, train_x, train_y) + + user_stat_spec = generate_stat_spec(type="table", X=test_x) + user_info = BaseUserInfo( + semantic_spec=self.user_semantic, stat_info={"RKMETableSpecification": user_stat_spec} + ) + logger.info(f"Searching Market for user: {user}_{idx}") + + search_result = self.market.search_learnware(user_info) + single_result = search_result.get_single_results() + multiple_result = search_result.get_multiple_results() + + logger.info(f"search result of user {user}_{idx}:") + logger.info( + f"single model num: {len(single_result)}, max_score: {single_result[0].score}, min_score: {single_result[-1].score}" + ) + + if len(multiple_result) > 0: + mixture_id = " ".join([learnware.id for learnware in multiple_result[0].learnwares]) + logger.info(f"mixture_score: {multiple_result[0].score}, mixture_learnware: {mixture_id}") + mixture_learnware_list = multiple_result[0].learnwares + else: + mixture_learnware_list = [single_result[0].learnware] + + test_info = {"user": user, "idx": idx, "train_subsets": train_subsets, "test_x": test_x, "test_y": test_y} + common_config = {"learnwares": mixture_learnware_list} + method_configs = { + "user_model": {"dataset": self.benchmark.name, "model_type": "lgb"}, + "homo_single_aug": {"single_learnware": [single_result[0].learnware]}, + "homo_ensemble_pruning": common_config + } + + for method_name in methods: + logger.info(f"Testing method {method_name}") + test_info["method_name"] = method_name + test_info["force"] = method_name in methods_to_retest + test_info.update(method_configs[method_name]) + self.test_method(test_info, recorders, loss_func=loss_func_rmse) + + for method, recorder in recorders.items(): + recorder.save(os.path.join(self.curves_result_path, f"{user}/{user}_{method}_performance.json")) - methods_to_plot = ["user_model", "homo_single_aug", "homo_ensemble_pruning"] - plot_performance_curves(self.curves_result_path, user, {method: recorders[method] for method in methods_to_plot}, task="Homo", n_labeled_list=homo_n_labeled_list) \ No newline at end of file + plot_performance_curves(self.curves_result_path, user, recorders, task="Homo", n_labeled_list=homo_n_labeled_list) \ No newline at end of file From a020fcf0379df6bd438d774dab88582c4e99fd27 Mon Sep 17 00:00:00 2001 From: liuht Date: Sun, 14 Jan 2024 18:29:15 +0800 Subject: [PATCH 24/32] [MNT] add skip test --- examples/dataset_table_workflow/hetero.py | 95 ++++++++++---------- examples/dataset_table_workflow/homo.py | 100 +++++++++++----------- 2 files changed, 98 insertions(+), 97 deletions(-) diff --git a/examples/dataset_table_workflow/hetero.py b/examples/dataset_table_workflow/hetero.py index 3caa5d3..a3112f7 100644 --- a/examples/dataset_table_workflow/hetero.py +++ b/examples/dataset_table_workflow/hetero.py @@ -106,60 +106,61 @@ class HeterogeneousDatasetWorkflow(TableWorkflow): ) - def labeled_hetero_table_example(self): + def labeled_hetero_table_example(self, skip_test=False): logger.info("Total Items: %d" % len(self.market)) methods = ["user_model", "hetero_single_aug", "hetero_multiple_avg", "hetero_ensemble_pruning"] recorders = {method: Recorder() for method in methods} - user = self.benchmark.name - for idx in range(self.benchmark.user_num): - test_x, test_y = self.benchmark.get_test_data(user_ids=idx) - test_x, test_y = test_x.values, test_y.values - - train_x, train_y = self.benchmark.get_train_data(user_ids=idx) - train_x, train_y, feature_descriptions = train_x.values, train_y.values, train_x.columns - train_subsets = self.get_train_subsets(hetero_n_labeled_list, hetero_n_repeat_list, train_x, train_y) - - user_stat_spec = generate_stat_spec(type="table", X=test_x) - input_description = { - "Dimension": len(feature_descriptions), - "Description": {str(i): feature_descriptions[i] for i in range(len(feature_descriptions))} - } - user_semantic["Input"] = input_description - user_info = BaseUserInfo( - semantic_spec=user_semantic, stat_info={user_stat_spec.type: user_stat_spec} - ) - logger.info(f"Searching Market for user: {user}_{idx}") + + if not skip_test: + for idx in range(self.benchmark.user_num): + test_x, test_y = self.benchmark.get_test_data(user_ids=idx) + test_x, test_y = test_x.values, test_y.values + + train_x, train_y = self.benchmark.get_train_data(user_ids=idx) + train_x, train_y, feature_descriptions = train_x.values, train_y.values, train_x.columns + train_subsets = self.get_train_subsets(hetero_n_labeled_list, hetero_n_repeat_list, train_x, train_y) + + user_stat_spec = generate_stat_spec(type="table", X=test_x) + input_description = { + "Dimension": len(feature_descriptions), + "Description": {str(i): feature_descriptions[i] for i in range(len(feature_descriptions))} + } + user_semantic["Input"] = input_description + user_info = BaseUserInfo( + semantic_spec=user_semantic, stat_info={user_stat_spec.type: user_stat_spec} + ) + logger.info(f"Searching Market for user: {user}_{idx}") - search_result = self.market.search_learnware(user_info) - single_result = search_result.get_single_results() - multiple_result = search_result.get_multiple_results() + search_result = self.market.search_learnware(user_info) + single_result = search_result.get_single_results() + multiple_result = search_result.get_multiple_results() - if len(multiple_result) > 0: - mixture_id = " ".join([learnware.id for learnware in multiple_result[0].learnwares]) - logger.info(f"Mixture score: {multiple_result[0].score}, Mixture learnware: {mixture_id}") - mixture_learnware_list = multiple_result[0].learnwares - else: - mixture_learnware_list = [single_result[0].learnware] - - logger.info(f"Hetero search result of user {user}_{idx}: mixture learnware num: {len(mixture_learnware_list)}") + if len(multiple_result) > 0: + mixture_id = " ".join([learnware.id for learnware in multiple_result[0].learnwares]) + logger.info(f"Mixture score: {multiple_result[0].score}, Mixture learnware: {mixture_id}") + mixture_learnware_list = multiple_result[0].learnwares + else: + mixture_learnware_list = [single_result[0].learnware] + + logger.info(f"Hetero search result of user {user}_{idx}: mixture learnware num: {len(mixture_learnware_list)}") - test_info = {"user": user, "idx": idx, "train_subsets": train_subsets, "test_x": test_x, "test_y": test_y, "n_labeled_list": hetero_n_labeled_list} - common_config = {"user_rkme": user_stat_spec, "learnwares": mixture_learnware_list} - method_configs = { - "user_model": {"dataset": self.benchmark.name, "model_type": "lgb"}, - "hetero_single_aug": {"user_rkme": user_stat_spec, "single_learnware": single_result[0].learnware}, - "hetero_multiple_avg": common_config, - "hetero_ensemble_pruning": common_config - } + test_info = {"user": user, "idx": idx, "train_subsets": train_subsets, "test_x": test_x, "test_y": test_y, "n_labeled_list": hetero_n_labeled_list} + common_config = {"user_rkme": user_stat_spec, "learnwares": mixture_learnware_list} + method_configs = { + "user_model": {"dataset": self.benchmark.name, "model_type": "lgb"}, + "hetero_single_aug": {"user_rkme": user_stat_spec, "single_learnware": single_result[0].learnware}, + "hetero_multiple_avg": common_config, + "hetero_ensemble_pruning": common_config + } - for method_name in methods: - logger.info(f"Testing method {method_name}") - test_info["method_name"] = method_name - test_info.update(method_configs[method_name]) - self.test_method(test_info, recorders, loss_func=loss_func_rmse) - - for method, recorder in recorders.items(): - recorder.save(os.path.join(self.curves_result_path, f"{user}/{user}_{method}_performance.json")) + for method_name in methods: + logger.info(f"Testing method {method_name}") + test_info["method_name"] = method_name + test_info.update(method_configs[method_name]) + self.test_method(test_info, recorders, loss_func=loss_func_rmse) + + for method, recorder in recorders.items(): + recorder.save(os.path.join(self.curves_result_path, f"{user}/{user}_{method}_performance.json")) plot_performance_curves(self.curves_result_path, user, recorders, task="Hetero", n_labeled_list=hetero_n_labeled_list) \ No newline at end of file diff --git a/examples/dataset_table_workflow/homo.py b/examples/dataset_table_workflow/homo.py index 73c0544..9d66f13 100644 --- a/examples/dataset_table_workflow/homo.py +++ b/examples/dataset_table_workflow/homo.py @@ -105,60 +105,60 @@ class HomogeneousDatasetWorkflow(TableWorkflow): ) - def labeled_homo_table_example(self): + def labeled_homo_table_example(self, skip_test=False): logger.info("Total Item: %d" % (len(self.market))) methods = ["user_model", "homo_single_aug", "homo_ensemble_pruning"] methods_to_retest = [] recorders = {method: Recorder() for method in methods} - user = self.benchmark.name - for idx in range(self.benchmark.user_num): - test_x, test_y = self.benchmark.get_test_data(user_ids=idx) - test_x, test_y = test_x.values, test_y.values - - train_x, train_y = self.benchmark.get_train_data(user_ids=idx) - train_x, train_y = train_x.values, train_y.values - train_subsets = self.get_train_subsets(homo_n_labeled_list, homo_n_repeat_list, train_x, train_y) - - user_stat_spec = generate_stat_spec(type="table", X=test_x) - user_info = BaseUserInfo( - semantic_spec=self.user_semantic, stat_info={"RKMETableSpecification": user_stat_spec} - ) - logger.info(f"Searching Market for user: {user}_{idx}") - - search_result = self.market.search_learnware(user_info) - single_result = search_result.get_single_results() - multiple_result = search_result.get_multiple_results() - - logger.info(f"search result of user {user}_{idx}:") - logger.info( - f"single model num: {len(single_result)}, max_score: {single_result[0].score}, min_score: {single_result[-1].score}" - ) - - if len(multiple_result) > 0: - mixture_id = " ".join([learnware.id for learnware in multiple_result[0].learnwares]) - logger.info(f"mixture_score: {multiple_result[0].score}, mixture_learnware: {mixture_id}") - mixture_learnware_list = multiple_result[0].learnwares - else: - mixture_learnware_list = [single_result[0].learnware] - - test_info = {"user": user, "idx": idx, "train_subsets": train_subsets, "test_x": test_x, "test_y": test_y} - common_config = {"learnwares": mixture_learnware_list} - method_configs = { - "user_model": {"dataset": self.benchmark.name, "model_type": "lgb"}, - "homo_single_aug": {"single_learnware": [single_result[0].learnware]}, - "homo_ensemble_pruning": common_config - } - - for method_name in methods: - logger.info(f"Testing method {method_name}") - test_info["method_name"] = method_name - test_info["force"] = method_name in methods_to_retest - test_info.update(method_configs[method_name]) - self.test_method(test_info, recorders, loss_func=loss_func_rmse) - for method, recorder in recorders.items(): - recorder.save(os.path.join(self.curves_result_path, f"{user}/{user}_{method}_performance.json")) + if not skip_test: + for idx in range(self.benchmark.user_num): + test_x, test_y = self.benchmark.get_test_data(user_ids=idx) + test_x, test_y = test_x.values, test_y.values + + train_x, train_y = self.benchmark.get_train_data(user_ids=idx) + train_x, train_y = train_x.values, train_y.values + train_subsets = self.get_train_subsets(homo_n_labeled_list, homo_n_repeat_list, train_x, train_y) + + user_stat_spec = generate_stat_spec(type="table", X=test_x) + user_info = BaseUserInfo( + semantic_spec=self.user_semantic, stat_info={"RKMETableSpecification": user_stat_spec} + ) + logger.info(f"Searching Market for user: {user}_{idx}") + + search_result = self.market.search_learnware(user_info) + single_result = search_result.get_single_results() + multiple_result = search_result.get_multiple_results() + + logger.info(f"search result of user {user}_{idx}:") + logger.info( + f"single model num: {len(single_result)}, max_score: {single_result[0].score}, min_score: {single_result[-1].score}" + ) + + if len(multiple_result) > 0: + mixture_id = " ".join([learnware.id for learnware in multiple_result[0].learnwares]) + logger.info(f"mixture_score: {multiple_result[0].score}, mixture_learnware: {mixture_id}") + mixture_learnware_list = multiple_result[0].learnwares + else: + mixture_learnware_list = [single_result[0].learnware] + + test_info = {"user": user, "idx": idx, "train_subsets": train_subsets, "test_x": test_x, "test_y": test_y} + common_config = {"learnwares": mixture_learnware_list} + method_configs = { + "user_model": {"dataset": self.benchmark.name, "model_type": "lgb"}, + "homo_single_aug": {"single_learnware": [single_result[0].learnware]}, + "homo_ensemble_pruning": common_config + } + + for method_name in methods: + logger.info(f"Testing method {method_name}") + test_info["method_name"] = method_name + test_info["force"] = method_name in methods_to_retest + test_info.update(method_configs[method_name]) + self.test_method(test_info, recorders, loss_func=loss_func_rmse) + + for method, recorder in recorders.items(): + recorder.save(os.path.join(self.curves_result_path, f"{user}/{user}_{method}_performance.json")) - methods_to_plot = ["user_model", "homo_single_aug", "homo_ensemble_pruning"] - plot_performance_curves(self.curves_result_path, user, {method: recorders[method] for method in methods_to_plot}, task="Homo", n_labeled_list=homo_n_labeled_list) \ No newline at end of file + plot_performance_curves(self.curves_result_path, user, recorders, task="Homo", n_labeled_list=homo_n_labeled_list) \ No newline at end of file From 04f98ffa5e254ffe2baeb69f3aecd81f22692b98 Mon Sep 17 00:00:00 2001 From: liuht Date: Sun, 14 Jan 2024 18:38:05 +0800 Subject: [PATCH 25/32] [FIX] merge failures --- examples/dataset_table_workflow/homo.py | 47 ++----------------------- 1 file changed, 2 insertions(+), 45 deletions(-) diff --git a/examples/dataset_table_workflow/homo.py b/examples/dataset_table_workflow/homo.py index 92305f5..71445ec 100644 --- a/examples/dataset_table_workflow/homo.py +++ b/examples/dataset_table_workflow/homo.py @@ -105,22 +105,13 @@ class HomogeneousDatasetWorkflow(TableWorkflow): ) - def labeled_homo_table_example(self, skip_test=False): + def labeled_homo_table_example(self, skip_test=True): logger.info("Total Item: %d" % (len(self.market))) methods = ["user_model", "homo_single_aug", "homo_ensemble_pruning"] methods_to_retest = [] recorders = {method: Recorder() for method in methods} user = self.benchmark.name - if not skip_test: - for idx in range(self.benchmark.user_num): - test_x, test_y = self.benchmark.get_test_data(user_ids=idx) - test_x, test_y = test_x.values, test_y.values - - train_x, train_y = self.benchmark.get_train_data(user_ids=idx) - train_x, train_y = train_x.values, train_y.values - train_subsets = self.get_train_subsets(homo_n_labeled_list, homo_n_repeat_list, train_x, train_y) - if not skip_test: for idx in range(self.benchmark.user_num): test_x, test_y = self.benchmark.get_test_data(user_ids=idx) @@ -130,10 +121,6 @@ class HomogeneousDatasetWorkflow(TableWorkflow): train_x, train_y = train_x.values, train_y.values train_subsets = self.get_train_subsets(homo_n_labeled_list, homo_n_repeat_list, train_x, train_y) - user_stat_spec = generate_stat_spec(type="table", X=test_x) - user_info = BaseUserInfo( - semantic_spec=self.user_semantic, stat_info={"RKMETableSpecification": user_stat_spec} - ) logger.info(f"Searching Market for user: {user}_{idx}") user_stat_spec = generate_stat_spec(type="table", X=test_x) user_info = BaseUserInfo( @@ -141,28 +128,15 @@ class HomogeneousDatasetWorkflow(TableWorkflow): ) logger.info(f"Searching Market for user: {user}_{idx}") - search_result = self.market.search_learnware(user_info) - single_result = search_result.get_single_results() - multiple_result = search_result.get_multiple_results() search_result = self.market.search_learnware(user_info) single_result = search_result.get_single_results() multiple_result = search_result.get_multiple_results() - logger.info(f"search result of user {user}_{idx}:") - logger.info( - f"single model num: {len(single_result)}, max_score: {single_result[0].score}, min_score: {single_result[-1].score}" - ) logger.info(f"search result of user {user}_{idx}:") logger.info( f"single model num: {len(single_result)}, max_score: {single_result[0].score}, min_score: {single_result[-1].score}" ) - if len(multiple_result) > 0: - mixture_id = " ".join([learnware.id for learnware in multiple_result[0].learnwares]) - logger.info(f"mixture_score: {multiple_result[0].score}, mixture_learnware: {mixture_id}") - mixture_learnware_list = multiple_result[0].learnwares - else: - mixture_learnware_list = [single_result[0].learnware] if len(multiple_result) > 0: mixture_id = " ".join([learnware.id for learnware in multiple_result[0].learnwares]) logger.info(f"mixture_score: {multiple_result[0].score}, mixture_learnware: {mixture_id}") @@ -177,13 +151,6 @@ class HomogeneousDatasetWorkflow(TableWorkflow): "homo_single_aug": {"single_learnware": [single_result[0].learnware]}, "homo_ensemble_pruning": common_config } - test_info = {"user": user, "idx": idx, "train_subsets": train_subsets, "test_x": test_x, "test_y": test_y} - common_config = {"learnwares": mixture_learnware_list} - method_configs = { - "user_model": {"dataset": self.benchmark.name, "model_type": "lgb"}, - "homo_single_aug": {"single_learnware": [single_result[0].learnware]}, - "homo_ensemble_pruning": common_config - } for method_name in methods: logger.info(f"Testing method {method_name}") @@ -194,15 +161,5 @@ class HomogeneousDatasetWorkflow(TableWorkflow): for method, recorder in recorders.items(): recorder.save(os.path.join(self.curves_result_path, f"{user}/{user}_{method}_performance.json")) - for method_name in methods: - logger.info(f"Testing method {method_name}") - test_info["method_name"] = method_name - test_info["force"] = method_name in methods_to_retest - test_info.update(method_configs[method_name]) - self.test_method(test_info, recorders, loss_func=loss_func_rmse) - - for method, recorder in recorders.items(): - recorder.save(os.path.join(self.curves_result_path, f"{user}/{user}_{method}_performance.json")) - - plot_performance_curves(self.curves_result_path, user, recorders, task="Homo", n_labeled_list=homo_n_labeled_list) + plot_performance_curves(self.curves_result_path, user, recorders, task="Homo", n_labeled_list=homo_n_labeled_list) \ No newline at end of file From b456b64e41eaf852eea7d3d121c7e1ff8d560d8f Mon Sep 17 00:00:00 2001 From: liuht Date: Sun, 14 Jan 2024 18:42:10 +0800 Subject: [PATCH 26/32] [FIX] merge failures --- examples/dataset_table_workflow/hetero.py | 2 +- examples/dataset_table_workflow/homo.py | 54 ++--------------------- 2 files changed, 5 insertions(+), 51 deletions(-) diff --git a/examples/dataset_table_workflow/hetero.py b/examples/dataset_table_workflow/hetero.py index a3112f7..4aa206e 100644 --- a/examples/dataset_table_workflow/hetero.py +++ b/examples/dataset_table_workflow/hetero.py @@ -130,8 +130,8 @@ class HeterogeneousDatasetWorkflow(TableWorkflow): user_info = BaseUserInfo( semantic_spec=user_semantic, stat_info={user_stat_spec.type: user_stat_spec} ) + logger.info(f"Searching Market for user: {user}_{idx}") - search_result = self.market.search_learnware(user_info) single_result = search_result.get_single_results() multiple_result = search_result.get_multiple_results() diff --git a/examples/dataset_table_workflow/homo.py b/examples/dataset_table_workflow/homo.py index 92305f5..1fd7403 100644 --- a/examples/dataset_table_workflow/homo.py +++ b/examples/dataset_table_workflow/homo.py @@ -108,7 +108,6 @@ class HomogeneousDatasetWorkflow(TableWorkflow): def labeled_homo_table_example(self, skip_test=False): logger.info("Total Item: %d" % (len(self.market))) methods = ["user_model", "homo_single_aug", "homo_ensemble_pruning"] - methods_to_retest = [] recorders = {method: Recorder() for method in methods} user = self.benchmark.name @@ -116,16 +115,7 @@ class HomogeneousDatasetWorkflow(TableWorkflow): for idx in range(self.benchmark.user_num): test_x, test_y = self.benchmark.get_test_data(user_ids=idx) test_x, test_y = test_x.values, test_y.values - - train_x, train_y = self.benchmark.get_train_data(user_ids=idx) - train_x, train_y = train_x.values, train_y.values - train_subsets = self.get_train_subsets(homo_n_labeled_list, homo_n_repeat_list, train_x, train_y) - - if not skip_test: - for idx in range(self.benchmark.user_num): - test_x, test_y = self.benchmark.get_test_data(user_ids=idx) - test_x, test_y = test_x.values, test_y.values - + train_x, train_y = self.benchmark.get_train_data(user_ids=idx) train_x, train_y = train_x.values, train_y.values train_subsets = self.get_train_subsets(homo_n_labeled_list, homo_n_repeat_list, train_x, train_y) @@ -134,35 +124,17 @@ class HomogeneousDatasetWorkflow(TableWorkflow): user_info = BaseUserInfo( semantic_spec=self.user_semantic, stat_info={"RKMETableSpecification": user_stat_spec} ) + logger.info(f"Searching Market for user: {user}_{idx}") - user_stat_spec = generate_stat_spec(type="table", X=test_x) - user_info = BaseUserInfo( - semantic_spec=self.user_semantic, stat_info={"RKMETableSpecification": user_stat_spec} - ) - logger.info(f"Searching Market for user: {user}_{idx}") - - search_result = self.market.search_learnware(user_info) - single_result = search_result.get_single_results() - multiple_result = search_result.get_multiple_results() search_result = self.market.search_learnware(user_info) single_result = search_result.get_single_results() multiple_result = search_result.get_multiple_results() - logger.info(f"search result of user {user}_{idx}:") - logger.info( - f"single model num: {len(single_result)}, max_score: {single_result[0].score}, min_score: {single_result[-1].score}" - ) logger.info(f"search result of user {user}_{idx}:") logger.info( f"single model num: {len(single_result)}, max_score: {single_result[0].score}, min_score: {single_result[-1].score}" ) - if len(multiple_result) > 0: - mixture_id = " ".join([learnware.id for learnware in multiple_result[0].learnwares]) - logger.info(f"mixture_score: {multiple_result[0].score}, mixture_learnware: {mixture_id}") - mixture_learnware_list = multiple_result[0].learnwares - else: - mixture_learnware_list = [single_result[0].learnware] if len(multiple_result) > 0: mixture_id = " ".join([learnware.id for learnware in multiple_result[0].learnwares]) logger.info(f"mixture_score: {multiple_result[0].score}, mixture_learnware: {mixture_id}") @@ -177,32 +149,14 @@ class HomogeneousDatasetWorkflow(TableWorkflow): "homo_single_aug": {"single_learnware": [single_result[0].learnware]}, "homo_ensemble_pruning": common_config } - test_info = {"user": user, "idx": idx, "train_subsets": train_subsets, "test_x": test_x, "test_y": test_y} - common_config = {"learnwares": mixture_learnware_list} - method_configs = { - "user_model": {"dataset": self.benchmark.name, "model_type": "lgb"}, - "homo_single_aug": {"single_learnware": [single_result[0].learnware]}, - "homo_ensemble_pruning": common_config - } for method_name in methods: logger.info(f"Testing method {method_name}") test_info["method_name"] = method_name - test_info["force"] = method_name in methods_to_retest test_info.update(method_configs[method_name]) self.test_method(test_info, recorders, loss_func=loss_func_rmse) - - for method, recorder in recorders.items(): - recorder.save(os.path.join(self.curves_result_path, f"{user}/{user}_{method}_performance.json")) - for method_name in methods: - logger.info(f"Testing method {method_name}") - test_info["method_name"] = method_name - test_info["force"] = method_name in methods_to_retest - test_info.update(method_configs[method_name]) - self.test_method(test_info, recorders, loss_func=loss_func_rmse) - + for method, recorder in recorders.items(): recorder.save(os.path.join(self.curves_result_path, f"{user}/{user}_{method}_performance.json")) - - plot_performance_curves(self.curves_result_path, user, recorders, task="Homo", n_labeled_list=homo_n_labeled_list) + plot_performance_curves(self.curves_result_path, user, recorders, task="Homo", n_labeled_list=homo_n_labeled_list) \ No newline at end of file From 69db08779a8054709aa09753cfc8cd29214cca8f Mon Sep 17 00:00:00 2001 From: liuht Date: Sun, 14 Jan 2024 19:04:04 +0800 Subject: [PATCH 27/32] [FIX] move skip_test into workflow.py --- examples/dataset_table_workflow/hetero.py | 2 +- examples/dataset_table_workflow/homo.py | 2 +- examples/dataset_table_workflow/workflow.py | 12 ++++++------ 3 files changed, 8 insertions(+), 8 deletions(-) diff --git a/examples/dataset_table_workflow/hetero.py b/examples/dataset_table_workflow/hetero.py index 4aa206e..0099e63 100644 --- a/examples/dataset_table_workflow/hetero.py +++ b/examples/dataset_table_workflow/hetero.py @@ -106,7 +106,7 @@ class HeterogeneousDatasetWorkflow(TableWorkflow): ) - def labeled_hetero_table_example(self, skip_test=False): + def labeled_hetero_table_example(self, skip_test): logger.info("Total Items: %d" % len(self.market)) methods = ["user_model", "hetero_single_aug", "hetero_multiple_avg", "hetero_ensemble_pruning"] recorders = {method: Recorder() for method in methods} diff --git a/examples/dataset_table_workflow/homo.py b/examples/dataset_table_workflow/homo.py index 0b44f8b..b22f4e0 100644 --- a/examples/dataset_table_workflow/homo.py +++ b/examples/dataset_table_workflow/homo.py @@ -105,7 +105,7 @@ class HomogeneousDatasetWorkflow(TableWorkflow): ) - def labeled_homo_table_example(self, skip_test=False): + def labeled_homo_table_example(self, skip_test): logger.info("Total Item: %d" % (len(self.market))) methods = ["user_model", "homo_single_aug", "homo_ensemble_pruning"] recorders = {method: Recorder() for method in methods} diff --git a/examples/dataset_table_workflow/workflow.py b/examples/dataset_table_workflow/workflow.py index e079b6e..93a6fcc 100644 --- a/examples/dataset_table_workflow/workflow.py +++ b/examples/dataset_table_workflow/workflow.py @@ -18,25 +18,25 @@ class TableDatasetWorkflow: ) workflow.unlabeled_homo_table_example() - def labeled_homo_table_example(self): + def labeled_homo_table_example(self, skip_test=False): workflow = HomogeneousDatasetWorkflow( benchmark_config=homo_table_benchmark_config, name="easy", rebuild=False ) - workflow.labeled_homo_table_example() + workflow.labeled_homo_table_example(skip_test=skip_test) def cross_feat_eng_hetero_table_example(self): set_seed(0) workflow = HeterogeneousDatasetWorkflow( benchmark_config=hetero_cross_feat_eng_benchmark_config, name="hetero", - rebuild=False, - retrain=False + rebuild=True, + retrain=True ) workflow.unlabeled_hetero_table_example() - def cross_task_hetero_table_example(self): + def cross_task_hetero_table_example(self, skip_test=False): set_seed(0) workflow = HeterogeneousDatasetWorkflow( benchmark_config=hetero_cross_task_benchmark_config, @@ -44,7 +44,7 @@ class TableDatasetWorkflow: rebuild=False, retrain=False ) - workflow.labeled_hetero_table_example() + workflow.labeled_hetero_table_example(skip_test=skip_test) if __name__ == "__main__": From e76f9fe696c4eca51849c7d88802325e4c2cd6a2 Mon Sep 17 00:00:00 2001 From: liuht Date: Mon, 15 Jan 2024 08:41:09 +0800 Subject: [PATCH 28/32] [MNT] fix random seed repeatedly --- examples/dataset_table_workflow/base.py | 1 + examples/dataset_table_workflow/hetero.py | 6 ++++-- examples/dataset_table_workflow/workflow.py | 6 ++---- 3 files changed, 7 insertions(+), 6 deletions(-) diff --git a/examples/dataset_table_workflow/base.py b/examples/dataset_table_workflow/base.py index 571f4fa..a10c69a 100644 --- a/examples/dataset_table_workflow/base.py +++ b/examples/dataset_table_workflow/base.py @@ -76,6 +76,7 @@ class TableWorkflow: self.user_semantic["Name"]["Values"] = "" if len(self.market) == 0 or rebuild == True: + if retrain: set_seed(0) for learnware_id in self.benchmark.learnware_ids: with tempfile.TemporaryDirectory(prefix="table_benchmark_") as tempdir: zip_path = os.path.join(tempdir, f"{learnware_id}.zip") diff --git a/examples/dataset_table_workflow/hetero.py b/examples/dataset_table_workflow/hetero.py index 0099e63..68842fc 100644 --- a/examples/dataset_table_workflow/hetero.py +++ b/examples/dataset_table_workflow/hetero.py @@ -11,13 +11,14 @@ from learnware.reuse import AveragingReuser, FeatureAlignLearnware from methods import * from base import TableWorkflow from config import align_model_params, user_semantic, hetero_n_labeled_list, hetero_n_repeat_list -from utils import Recorder, plot_performance_curves +from utils import Recorder, plot_performance_curves, set_seed logger = get_module_logger("hetero_test", level="INFO") class HeterogeneousDatasetWorkflow(TableWorkflow): def unlabeled_hetero_table_example(self): + set_seed(0) logger.info("Total Item: %d" % len(self.market)) learnware_rmse_list = [] single_score_list = [] @@ -39,7 +40,7 @@ class HeterogeneousDatasetWorkflow(TableWorkflow): ) logger.info(f"Searching Market for user: {user}_{idx}") - search_result = self.market.search_learnware(user_info, max_search_num=10) + search_result = self.market.search_learnware(user_info, search_method="auto") single_result = search_result.get_single_results() multiple_result = search_result.get_multiple_results() @@ -107,6 +108,7 @@ class HeterogeneousDatasetWorkflow(TableWorkflow): def labeled_hetero_table_example(self, skip_test): + set_seed(0) logger.info("Total Items: %d" % len(self.market)) methods = ["user_model", "hetero_single_aug", "hetero_multiple_avg", "hetero_ensemble_pruning"] recorders = {method: Recorder() for method in methods} diff --git a/examples/dataset_table_workflow/workflow.py b/examples/dataset_table_workflow/workflow.py index 93a6fcc..dee3fc9 100644 --- a/examples/dataset_table_workflow/workflow.py +++ b/examples/dataset_table_workflow/workflow.py @@ -27,7 +27,6 @@ class TableDatasetWorkflow: workflow.labeled_homo_table_example(skip_test=skip_test) def cross_feat_eng_hetero_table_example(self): - set_seed(0) workflow = HeterogeneousDatasetWorkflow( benchmark_config=hetero_cross_feat_eng_benchmark_config, name="hetero", @@ -37,12 +36,11 @@ class TableDatasetWorkflow: workflow.unlabeled_hetero_table_example() def cross_task_hetero_table_example(self, skip_test=False): - set_seed(0) workflow = HeterogeneousDatasetWorkflow( benchmark_config=hetero_cross_task_benchmark_config, name="hetero", - rebuild=False, - retrain=False + rebuild=True, + retrain=True ) workflow.labeled_hetero_table_example(skip_test=skip_test) From 852b466b95b7c2bbe8f071d79f4d19161b439f6b Mon Sep 17 00:00:00 2001 From: Gene Date: Mon, 15 Jan 2024 11:13:58 +0800 Subject: [PATCH 29/32] [FIX] enable the code pass the flake8 test --- examples/dataset_table_workflow/base.py | 50 +- examples/dataset_table_workflow/config.py | 785 ++++++++++++++++----- examples/dataset_table_workflow/hetero.py | 68 +- examples/dataset_table_workflow/homo.py | 39 +- examples/dataset_table_workflow/methods.py | 24 +- examples/dataset_table_workflow/train.py | 8 +- examples/dataset_table_workflow/utils.py | 32 +- 7 files changed, 732 insertions(+), 274 deletions(-) diff --git a/examples/dataset_table_workflow/base.py b/examples/dataset_table_workflow/base.py index a10c69a..7e619ef 100644 --- a/examples/dataset_table_workflow/base.py +++ b/examples/dataset_table_workflow/base.py @@ -1,6 +1,5 @@ import os import time -import torch import random import requests import tempfile @@ -12,8 +11,8 @@ from learnware.market import instantiate_learnware_market from learnware.reuse.utils import fill_data_with_mean from learnware.tests.benchmarks import LearnwareBenchmark -from config import * -from methods import * +from config import market_mapping_params +from methods import loss_func_rmse, test_methods from utils import set_seed logger = get_module_logger("base_table", level="INFO") @@ -26,14 +25,14 @@ class TableWorkflow: self.curves_result_path = os.path.join(self.result_path, "curves") os.makedirs(self.result_path, exist_ok=True) os.makedirs(self.curves_result_path, exist_ok=True) - self._prepare_market(benchmark_config, name, rebuild, retrain) - + self._prepare_market(benchmark_config, name, rebuild, retrain) + @staticmethod def _limited_data(method, test_info, loss_func): def subset_generator(): for subset in test_info["train_subsets"]: yield subset - + all_scores = [] for subset in subset_generator(): subset_scores = [] @@ -43,7 +42,7 @@ class TableWorkflow: subset_scores.append(loss_func(model.predict(test_info["test_x"]), test_info["test_y"])) all_scores.append(subset_scores) return all_scores - + @staticmethod def get_train_subsets(n_labeled_list, n_repeat_list, train_x, train_y): np.random.seed(1) @@ -56,9 +55,11 @@ class TableWorkflow: n_label = len(train_x) for _ in range(repeated): subset_idxs = np.random.choice(len(train_x), n_label, replace=False) - train_subsets[-1].append({"x_train": np.array(train_x[subset_idxs]), "y_train": np.array(train_y[subset_idxs])}) + train_subsets[-1].append( + {"x_train": np.array(train_x[subset_idxs]), "y_train": np.array(train_y[subset_idxs])} + ) return train_subsets - + def _prepare_market(self, benchmark_config, name, rebuild, retrain): client = LearnwareClient() self.benchmark = LearnwareBenchmark().get_benchmark(benchmark_config) @@ -69,14 +70,17 @@ class TableWorkflow: organizer_kwargs={ "auto_update": True, "auto_update_limit": len(self.benchmark.learnware_ids), - **market_mapping_params - } if retrain else None + **market_mapping_params, + } + if retrain + else None, ) self.user_semantic = client.get_semantic_specification(self.benchmark.learnware_ids[0]) self.user_semantic["Name"]["Values"] = "" - - if len(self.market) == 0 or rebuild == True: - if retrain: set_seed(0) + + if len(self.market) == 0 or rebuild is True: + if retrain: + set_seed(0) for learnware_id in self.benchmark.learnware_ids: with tempfile.TemporaryDirectory(prefix="table_benchmark_") as tempdir: zip_path = os.path.join(tempdir, f"{learnware_id}.zip") @@ -87,24 +91,28 @@ class TableWorkflow: self.market.add_learnware(zip_path, semantic_spec) break except (requests.exceptions.RequestException, IOError, Exception) as e: - logger.info(f"An error occurred when downloading {learnware_id}: {e}\n{traceback.format_exc()}, retrying...") + logger.info( + f"An error occurred when downloading {learnware_id}: {e}\n{traceback.format_exc()}, retrying..." + ) time.sleep(1) continue - + def test_method(self, test_info, recorders, loss_func=loss_func_rmse): method_name_full = test_info["method_name"] - method_name = method_name_full if method_name_full == "user_model" else "_".join(method_name_full.split("_")[1:]) + method_name = ( + method_name_full if method_name_full == "user_model" else "_".join(method_name_full.split("_")[1:]) + ) method = test_methods[method_name_full] user, idx = test_info["user"], test_info["idx"] recorder = recorders[method_name_full] - + save_root_path = os.path.join(self.curves_result_path, f"{user}/{user}_{idx}") os.makedirs(save_root_path, exist_ok=True) save_path = os.path.join(save_root_path, f"{method_name}.json") - + if recorder.should_test_method(user, idx, save_path): scores = self._limited_data(method, test_info, loss_func) recorder.record(user, scores) recorder.save(save_path) - - logger.info(f"Method {method_name} on {user}_{idx} finished") \ No newline at end of file + + logger.info(f"Method {method_name} on {user}_{idx} finished") diff --git a/examples/dataset_table_workflow/config.py b/examples/dataset_table_workflow/config.py index 7f0d5de..5c27d33 100644 --- a/examples/dataset_table_workflow/config.py +++ b/examples/dataset_table_workflow/config.py @@ -17,23 +17,23 @@ user_semantic = { } styles = { - 'user_model': {"color": "navy", "marker": "o", "linestyle": "-"}, - 'select_score': {'color': 'gold', 'marker': 's', 'linestyle': '--'}, - 'oracle_score': {'color': 'darkorange', 'marker': '^', 'linestyle': '-.'}, - 'mean_score': {'color': 'gray', 'marker': 'x', 'linestyle': ':'}, - 'single_aug': {'color': 'gold', 'marker': 's', 'linestyle': '--'}, - 'multiple_avg': {'color': 'blue', 'marker': '*', 'linestyle': '-'}, - 'multiple_aug': {'color': 'purple', 'marker': 'd', 'linestyle': '--'}, - 'ensemble_pruning': {"color": "magenta", "marker": "d", "linestyle": "-."} + "user_model": {"color": "navy", "marker": "o", "linestyle": "-"}, + "select_score": {"color": "gold", "marker": "s", "linestyle": "--"}, + "oracle_score": {"color": "darkorange", "marker": "^", "linestyle": "-."}, + "mean_score": {"color": "gray", "marker": "x", "linestyle": ":"}, + "single_aug": {"color": "gold", "marker": "s", "linestyle": "--"}, + "multiple_avg": {"color": "blue", "marker": "*", "linestyle": "-"}, + "multiple_aug": {"color": "purple", "marker": "d", "linestyle": "--"}, + "ensemble_pruning": {"color": "magenta", "marker": "d", "linestyle": "-."}, } labels = { - 'user_model': "User Model", - 'single_aug': "Single Learnware Reuse (FeatAug)", + "user_model": "User Model", + "single_aug": "Single Learnware Reuse (FeatAug)", "select_score": "Single Learnware Reuse (FeatAug)", - 'multiple_aug': "Multiple Learnware Reuse (FeatAug)", - 'ensemble_pruning': "Multiple Learnware Reuse (EnsemblePrune)", - 'multiple_avg': "Multiple Learnware Reuse (Averaging)" + "multiple_aug": "Multiple Learnware Reuse (FeatAug)", + "ensemble_pruning": "Multiple Learnware Reuse (EnsemblePrune)", + "multiple_avg": "Multiple Learnware Reuse (Averaging)", } align_model_params = { @@ -47,9 +47,9 @@ align_model_params = { } market_mapping_params = { - "lr": 1e-4, + "lr": 1e-4, "num_epoch": 50, - "batch_size": 64, + "batch_size": 64, "num_partition": 2, # num of column partitions for pos/neg sampling "overlap_ratio": 0.7, # specify the overlap ratio of column partitions during the CL "hidden_dim": 256, # the dimension of hidden embeddings @@ -73,10 +73,10 @@ user_model_params = { "n_estimators": 100000, "boost_from_average": False, "num_threads": 32, - "verbose": -1 + "verbose": -1, }, "MAX_ROUNDS": 1000, - "early_stopping_rounds": 1000 + "early_stopping_rounds": 1000, } }, "PFS": { @@ -95,7 +95,7 @@ user_model_params = { "n_estimators": 100000, }, "MAX_ROUNDS": 1000, - "early_stopping_rounds": 1000 + "early_stopping_rounds": 1000, } }, "PFS_HOMO": { @@ -114,185 +114,620 @@ user_model_params = { "n_estimators": 100000, }, "MAX_ROUNDS": 1000, - "early_stopping_rounds": 1000 + "early_stopping_rounds": 1000, } - } + }, } homo_table_benchmark_config = BenchmarkConfig( name="PFS_HOMO", user_num=53, learnware_ids=[ - "00002265", "00002266", "00002267", "00002268", - "00002269", "00002270", "00002271", "00002272", - "00002273", "00002274", "00002275", "00002276", - "00002277", "00002278", "00002279", "00002280", - "00002281", "00002282", "00002283", "00002284", - "00002285", "00002286", "00002287", "00002288", - "00002289", "00002290", "00002291", "00002292", - "00002293", "00002294", "00002295", "00002296", - "00002297", "00002298", "00002299", "00002300", - "00002301", "00002302", "00002303", "00002304", - "00002305", "00002306", "00002307", "00002308", - "00002309", "00002310", "00002311", "00002312", - "00002313", "00002314", "00002315", "00002316", - "00002317" + "00002265", + "00002266", + "00002267", + "00002268", + "00002269", + "00002270", + "00002271", + "00002272", + "00002273", + "00002274", + "00002275", + "00002276", + "00002277", + "00002278", + "00002279", + "00002280", + "00002281", + "00002282", + "00002283", + "00002284", + "00002285", + "00002286", + "00002287", + "00002288", + "00002289", + "00002290", + "00002291", + "00002292", + "00002293", + "00002294", + "00002295", + "00002296", + "00002297", + "00002298", + "00002299", + "00002300", + "00002301", + "00002302", + "00002303", + "00002304", + "00002305", + "00002306", + "00002307", + "00002308", + "00002309", + "00002310", + "00002311", + "00002312", + "00002313", + "00002314", + "00002315", + "00002316", + "00002317", ], test_data_path="PFS_HOMO/test_data.zip", train_data_path="PFS_HOMO/train_data.zip", - extra_info_path="PFS_HOMO/extra_info.zip" + extra_info_path="PFS_HOMO/extra_info.zip", ) -hetero_cross_feat_eng_benchmark_config = BenchmarkConfig( +hetero_cross_feat_eng_benchmark_config = BenchmarkConfig( name="PFS", user_num=41, - learnware_ids = [ - "00000342", "00000343", "00000344", "00000345", - "00000346", "00000347", "00000348", "00000349", - "00000350", "00000351", "00000352", "00000353", - "00000354", "00000355", "00000356", "00000357", - "00000358", "00000359", "00000360", "00000361", - "00000362", "00000363", "00000364", "00000365", - "00000366", "00000367", "00000368", "00000369", - "00000370", "00000371", "00000372", "00000373", - "00000374", "00000375", "00000376", "00000377", - "00000378", "00000379", "00000380", "00000381", - "00000382", "00000383", "00000384", "00000385", - "00000386", "00000387", "00000388", "00000389", - "00000390", "00000391", "00000392", "00000393", - "00000394", "00000395", "00000396", "00000397", - "00000398", "00000399", "00000400", "00000401", - "00000402", "00000403", "00000404", "00000405", - "00000406", "00000407", "00000408", "00000409", - "00000410", "00000411", "00000412", "00000413", - "00000414", "00000415", "00000416", "00000417", - "00000418", "00000419", "00000420", "00000421", - "00000422", "00000423", "00000424", "00000425", - "00000426", "00000427", "00000428", "00000429", - "00000430", "00000431", "00000432", "00000433", - "00000434", "00000435", "00000436", "00000437", - "00000438", "00000439", "00000440", "00000441", - "00000442", "00000443", "00000444", "00000730", - "00000731", "00000732", "00000733", "00000734", - "00000735", "00000736", "00000737", "00000738", - "00000739", "00000740", "00000741", "00000742", - "00000743", "00000744", "00000745", "00000746", - "00000747", "00000748", "00000749", "00000750", - "00000751", "00000752", "00000753", "00000754", - "00000755", "00000756", "00000757", "00000758", - "00000759", "00000760", "00000761", "00000762", - "00000763", "00000764", "00000765", "00000766", - "00000767", "00000768", "00000769", "00000770", - "00000771", "00000772", "00000773", "00000774", - "00000775", "00000776", "00000777", "00000778", - "00000779", "00000780", "00000781", "00000782", - "00000783", "00000786", "00000787", "00000788", - "00000789", "00000790", "00000791", "00000792", - "00000793", "00000794", "00000795", "00000796", - "00000797", "00000798", "00000799", "00000800", - "00000801", "00000802", "00000803", "00000804", - "00000805", "00000806", "00000807", "00000808", - "00000809", "00000810", "00000811", "00000812", - "00000813", "00000814", "00000815", "00000816", - "00000817", "00000818", "00000819", "00000820", - "00000821", "00000822", "00000823", "00000824", - "00000825", "00000826", "00000827", "00000828", - "00000829", "00000830", "00000831", "00000832", - "00000833", "00000834", "00000835", "00000836", - "00000837", "00000838", "00000839", "00000859", - "00000860", "00000861", "00000862", "00000863", - "00000864", "00000865", "00000866", "00000867", - "00000868", "00000869", "00000870", "00000871", - "00000872", "00000873", "00000874", "00000875", - "00000876", "00000877", "00000878", "00000879", - "00000880", "00000881", "00000882", "00000883", - "00000884", "00000885", "00000886", "00000887", - "00000888", "00000889", "00000890", "00000891", - "00000892", "00000893", "00000894", "00000895", - "00000896", "00000897", "00000898", "00000899", - "00000900", "00000901", "00000902", "00000903", - "00000904", "00000905", "00000906", "00000907", - "00000908", "00000909", "00000910", "00000911", - "00000912" + learnware_ids=[ + "00000342", + "00000343", + "00000344", + "00000345", + "00000346", + "00000347", + "00000348", + "00000349", + "00000350", + "00000351", + "00000352", + "00000353", + "00000354", + "00000355", + "00000356", + "00000357", + "00000358", + "00000359", + "00000360", + "00000361", + "00000362", + "00000363", + "00000364", + "00000365", + "00000366", + "00000367", + "00000368", + "00000369", + "00000370", + "00000371", + "00000372", + "00000373", + "00000374", + "00000375", + "00000376", + "00000377", + "00000378", + "00000379", + "00000380", + "00000381", + "00000382", + "00000383", + "00000384", + "00000385", + "00000386", + "00000387", + "00000388", + "00000389", + "00000390", + "00000391", + "00000392", + "00000393", + "00000394", + "00000395", + "00000396", + "00000397", + "00000398", + "00000399", + "00000400", + "00000401", + "00000402", + "00000403", + "00000404", + "00000405", + "00000406", + "00000407", + "00000408", + "00000409", + "00000410", + "00000411", + "00000412", + "00000413", + "00000414", + "00000415", + "00000416", + "00000417", + "00000418", + "00000419", + "00000420", + "00000421", + "00000422", + "00000423", + "00000424", + "00000425", + "00000426", + "00000427", + "00000428", + "00000429", + "00000430", + "00000431", + "00000432", + "00000433", + "00000434", + "00000435", + "00000436", + "00000437", + "00000438", + "00000439", + "00000440", + "00000441", + "00000442", + "00000443", + "00000444", + "00000730", + "00000731", + "00000732", + "00000733", + "00000734", + "00000735", + "00000736", + "00000737", + "00000738", + "00000739", + "00000740", + "00000741", + "00000742", + "00000743", + "00000744", + "00000745", + "00000746", + "00000747", + "00000748", + "00000749", + "00000750", + "00000751", + "00000752", + "00000753", + "00000754", + "00000755", + "00000756", + "00000757", + "00000758", + "00000759", + "00000760", + "00000761", + "00000762", + "00000763", + "00000764", + "00000765", + "00000766", + "00000767", + "00000768", + "00000769", + "00000770", + "00000771", + "00000772", + "00000773", + "00000774", + "00000775", + "00000776", + "00000777", + "00000778", + "00000779", + "00000780", + "00000781", + "00000782", + "00000783", + "00000786", + "00000787", + "00000788", + "00000789", + "00000790", + "00000791", + "00000792", + "00000793", + "00000794", + "00000795", + "00000796", + "00000797", + "00000798", + "00000799", + "00000800", + "00000801", + "00000802", + "00000803", + "00000804", + "00000805", + "00000806", + "00000807", + "00000808", + "00000809", + "00000810", + "00000811", + "00000812", + "00000813", + "00000814", + "00000815", + "00000816", + "00000817", + "00000818", + "00000819", + "00000820", + "00000821", + "00000822", + "00000823", + "00000824", + "00000825", + "00000826", + "00000827", + "00000828", + "00000829", + "00000830", + "00000831", + "00000832", + "00000833", + "00000834", + "00000835", + "00000836", + "00000837", + "00000838", + "00000839", + "00000859", + "00000860", + "00000861", + "00000862", + "00000863", + "00000864", + "00000865", + "00000866", + "00000867", + "00000868", + "00000869", + "00000870", + "00000871", + "00000872", + "00000873", + "00000874", + "00000875", + "00000876", + "00000877", + "00000878", + "00000879", + "00000880", + "00000881", + "00000882", + "00000883", + "00000884", + "00000885", + "00000886", + "00000887", + "00000888", + "00000889", + "00000890", + "00000891", + "00000892", + "00000893", + "00000894", + "00000895", + "00000896", + "00000897", + "00000898", + "00000899", + "00000900", + "00000901", + "00000902", + "00000903", + "00000904", + "00000905", + "00000906", + "00000907", + "00000908", + "00000909", + "00000910", + "00000911", + "00000912", ], test_data_path="PFS/test_data.zip", train_data_path="PFS/train_data.zip", - extra_info_path="PFS/extra_info.zip" + extra_info_path="PFS/extra_info.zip", ) hetero_cross_task_benchmark_config = BenchmarkConfig( name="M5", user_num=30, - learnware_ids = [ - "00000342", "00000343", "00000344", "00000345", - "00000346", "00000347", "00000348", "00000349", - "00000350", "00000351", "00000352", "00000353", - "00000354", "00000355", "00000356", "00000357", - "00000358", "00000359", "00000360", "00000361", - "00000362", "00000363", "00000364", "00000365", - "00000366", "00000367", "00000368", "00000369", - "00000370", "00000371", "00000372", "00000373", - "00000374", "00000375", "00000376", "00000377", - "00000378", "00000379", "00000380", "00000381", - "00000382", "00000383", "00000384", "00000385", - "00000386", "00000387", "00000388", "00000389", - "00000390", "00000391", "00000392", "00000393", - "00000394", "00000395", "00000396", "00000397", - "00000398", "00000399", "00000400", "00000401", - "00000402", "00000403", "00000404", "00000405", - "00000406", "00000407", "00000408", "00000409", - "00000410", "00000411", "00000412", "00000413", - "00000414", "00000415", "00000416", "00000417", - "00000418", "00000419", "00000420", "00000421", - "00000422", "00000423", "00000424", "00000425", - "00000426", "00000427", "00000428", "00000429", - "00000430", "00000431", "00000432", "00000433", - "00000434", "00000435", "00000436", "00000437", - "00000438", "00000439", "00000440", "00000441", - "00000442", "00000443", "00000444", "00000730", - "00000731", "00000732", "00000733", "00000734", - "00000735", "00000736", "00000737", "00000738", - "00000739", "00000740", "00000741", "00000742", - "00000743", "00000744", "00000745", "00000746", - "00000747", "00000748", "00000749", "00000750", - "00000751", "00000752", "00000753", "00000754", - "00000755", "00000756", "00000757", "00000758", - "00000759", "00000760", "00000761", "00000762", - "00000763", "00000764", "00000765", "00000766", - "00000767", "00000768", "00000769", "00000770", - "00000771", "00000772", "00000773", "00000774", - "00000775", "00000776", "00000777", "00000778", - "00000779", "00000780", "00000781", "00000782", - "00000783", "00000786", "00000787", "00000788", - "00000789", "00000790", "00000791", "00000792", - "00000793", "00000794", "00000795", "00000796", - "00000797", "00000798", "00000799", "00000800", - "00000801", "00000802", "00000803", "00000804", - "00000805", "00000806", "00000807", "00000808", - "00000809", "00000810", "00000811", "00000812", - "00000813", "00000814", "00000815", "00000816", - "00000817", "00000818", "00000819", "00000820", - "00000821", "00000822", "00000823", "00000824", - "00000825", "00000826", "00000827", "00000828", - "00000829", "00000830", "00000831", "00000832", - "00000833", "00000834", "00000835", "00000836", - "00000837", "00000838", "00000839", "00000859", - "00000860", "00000861", "00000862", "00000863", - "00000864", "00000865", "00000866", "00000867", - "00000868", "00000869", "00000870", "00000871", - "00000872", "00000873", "00000874", "00000875", - "00000876", "00000877", "00000878", "00000879", - "00000880", "00000881", "00000882", "00000883", - "00000884", "00000885", "00000886", "00000887", - "00000888", "00000889", "00000890", "00000891", - "00000892", "00000893", "00000894", "00000895", - "00000896", "00000897", "00000898", "00000899", - "00000900", "00000901", "00000902", "00000903", - "00000904", "00000905", "00000906", "00000907", - "00000908", "00000909", "00000910", "00000911", - "00000912" + learnware_ids=[ + "00000342", + "00000343", + "00000344", + "00000345", + "00000346", + "00000347", + "00000348", + "00000349", + "00000350", + "00000351", + "00000352", + "00000353", + "00000354", + "00000355", + "00000356", + "00000357", + "00000358", + "00000359", + "00000360", + "00000361", + "00000362", + "00000363", + "00000364", + "00000365", + "00000366", + "00000367", + "00000368", + "00000369", + "00000370", + "00000371", + "00000372", + "00000373", + "00000374", + "00000375", + "00000376", + "00000377", + "00000378", + "00000379", + "00000380", + "00000381", + "00000382", + "00000383", + "00000384", + "00000385", + "00000386", + "00000387", + "00000388", + "00000389", + "00000390", + "00000391", + "00000392", + "00000393", + "00000394", + "00000395", + "00000396", + "00000397", + "00000398", + "00000399", + "00000400", + "00000401", + "00000402", + "00000403", + "00000404", + "00000405", + "00000406", + "00000407", + "00000408", + "00000409", + "00000410", + "00000411", + "00000412", + "00000413", + "00000414", + "00000415", + "00000416", + "00000417", + "00000418", + "00000419", + "00000420", + "00000421", + "00000422", + "00000423", + "00000424", + "00000425", + "00000426", + "00000427", + "00000428", + "00000429", + "00000430", + "00000431", + "00000432", + "00000433", + "00000434", + "00000435", + "00000436", + "00000437", + "00000438", + "00000439", + "00000440", + "00000441", + "00000442", + "00000443", + "00000444", + "00000730", + "00000731", + "00000732", + "00000733", + "00000734", + "00000735", + "00000736", + "00000737", + "00000738", + "00000739", + "00000740", + "00000741", + "00000742", + "00000743", + "00000744", + "00000745", + "00000746", + "00000747", + "00000748", + "00000749", + "00000750", + "00000751", + "00000752", + "00000753", + "00000754", + "00000755", + "00000756", + "00000757", + "00000758", + "00000759", + "00000760", + "00000761", + "00000762", + "00000763", + "00000764", + "00000765", + "00000766", + "00000767", + "00000768", + "00000769", + "00000770", + "00000771", + "00000772", + "00000773", + "00000774", + "00000775", + "00000776", + "00000777", + "00000778", + "00000779", + "00000780", + "00000781", + "00000782", + "00000783", + "00000786", + "00000787", + "00000788", + "00000789", + "00000790", + "00000791", + "00000792", + "00000793", + "00000794", + "00000795", + "00000796", + "00000797", + "00000798", + "00000799", + "00000800", + "00000801", + "00000802", + "00000803", + "00000804", + "00000805", + "00000806", + "00000807", + "00000808", + "00000809", + "00000810", + "00000811", + "00000812", + "00000813", + "00000814", + "00000815", + "00000816", + "00000817", + "00000818", + "00000819", + "00000820", + "00000821", + "00000822", + "00000823", + "00000824", + "00000825", + "00000826", + "00000827", + "00000828", + "00000829", + "00000830", + "00000831", + "00000832", + "00000833", + "00000834", + "00000835", + "00000836", + "00000837", + "00000838", + "00000839", + "00000859", + "00000860", + "00000861", + "00000862", + "00000863", + "00000864", + "00000865", + "00000866", + "00000867", + "00000868", + "00000869", + "00000870", + "00000871", + "00000872", + "00000873", + "00000874", + "00000875", + "00000876", + "00000877", + "00000878", + "00000879", + "00000880", + "00000881", + "00000882", + "00000883", + "00000884", + "00000885", + "00000886", + "00000887", + "00000888", + "00000889", + "00000890", + "00000891", + "00000892", + "00000893", + "00000894", + "00000895", + "00000896", + "00000897", + "00000898", + "00000899", + "00000900", + "00000901", + "00000902", + "00000903", + "00000904", + "00000905", + "00000906", + "00000907", + "00000908", + "00000909", + "00000910", + "00000911", + "00000912", ], test_data_path="M5/test_data.zip", train_data_path="M5/train_data.zip", - extra_info_path="M5/extra_info.zip" -) \ No newline at end of file + extra_info_path="M5/extra_info.zip", +) diff --git a/examples/dataset_table_workflow/hetero.py b/examples/dataset_table_workflow/hetero.py index 68842fc..423f7b9 100644 --- a/examples/dataset_table_workflow/hetero.py +++ b/examples/dataset_table_workflow/hetero.py @@ -1,6 +1,5 @@ import os import warnings -warnings.filterwarnings("ignore") import numpy as np from learnware.logger import get_module_logger @@ -8,11 +7,12 @@ from learnware.specification import generate_stat_spec from learnware.market import BaseUserInfo from learnware.reuse import AveragingReuser, FeatureAlignLearnware -from methods import * +from methods import loss_func_rmse from base import TableWorkflow from config import align_model_params, user_semantic, hetero_n_labeled_list, hetero_n_repeat_list from utils import Recorder, plot_performance_curves, set_seed +warnings.filterwarnings("ignore") logger = get_module_logger("hetero_test", level="INFO") @@ -24,7 +24,7 @@ class HeterogeneousDatasetWorkflow(TableWorkflow): single_score_list = [] ensemble_score_list = [] all_learnwares = self.market.get_learnwares() - + user = self.benchmark.name for idx in range(self.benchmark.user_num): test_x, test_y = self.benchmark.get_test_data(user_ids=idx) @@ -32,23 +32,21 @@ class HeterogeneousDatasetWorkflow(TableWorkflow): user_stat_spec = generate_stat_spec(type="table", X=test_x) input_description = { "Dimension": len(feature_descriptions), - "Description": {str(i): feature_descriptions[i] for i in range(len(feature_descriptions))} + "Description": {str(i): feature_descriptions[i] for i in range(len(feature_descriptions))}, } user_semantic["Input"] = input_description - user_info = BaseUserInfo( - semantic_spec=user_semantic, stat_info={user_stat_spec.type: user_stat_spec} - ) + user_info = BaseUserInfo(semantic_spec=user_semantic, stat_info={user_stat_spec.type: user_stat_spec}) logger.info(f"Searching Market for user: {user}_{idx}") - + search_result = self.market.search_learnware(user_info, search_method="auto") single_result = search_result.get_single_results() multiple_result = search_result.get_multiple_results() - + logger.info(f"hetero search result of user {user}_{idx}: {single_result[0].learnware.id}") logger.info( f"single model num: {len(single_result)}, max_score: {single_result[0].score}, min_score: {single_result[-1].score}" ) - + single_hetero_learnware = FeatureAlignLearnware(single_result[0].learnware, **align_model_params) single_hetero_learnware.align(user_rkme=user_stat_spec) pred_y = single_hetero_learnware.predict(test_x) @@ -59,11 +57,11 @@ class HeterogeneousDatasetWorkflow(TableWorkflow): hetero_learnware = FeatureAlignLearnware(learnware, **align_model_params) hetero_learnware.align(user_rkme=user_stat_spec) pred_y = hetero_learnware.predict(test_x) - rmse_list.append(loss_func_rmse(pred_y, test_y)) + rmse_list.append(loss_func_rmse(pred_y, test_y)) logger.info( f"Top1-score: {single_result[0].score}, learnware_id: {single_result[0].learnware.id}, rmse: {single_score_list[0]}" ) - + if len(multiple_result) > 0: mixture_id = " ".join([learnware.id for learnware in multiple_result[0].learnwares]) logger.info(f"mixture_score: {multiple_result[0].score}, mixture_learnware: {mixture_id}") @@ -85,11 +83,11 @@ class HeterogeneousDatasetWorkflow(TableWorkflow): logger.info(f"mixture reuse rmse (ensemble): {ensemble_score}") learnware_rmse_list.append(rmse_list) - + single_list = np.array(learnware_rmse_list) avg_score_list = [np.mean(lst, axis=0) for lst in single_list] oracle_score_list = [np.min(lst, axis=0) for lst in single_list] - + logger.info( "RMSE of selected learnware: %.3f +/- %.3f, Average performance: %.3f +/- %.3f, Oracle performace: %.3f +/- %.3f" % ( @@ -98,7 +96,7 @@ class HeterogeneousDatasetWorkflow(TableWorkflow): np.mean(avg_score_list), np.std(avg_score_list), np.mean(oracle_score_list), - np.std(oracle_score_list) + np.std(oracle_score_list), ) ) logger.info( @@ -106,33 +104,30 @@ class HeterogeneousDatasetWorkflow(TableWorkflow): % (np.mean(ensemble_score_list), np.std(ensemble_score_list)) ) - def labeled_hetero_table_example(self, skip_test): set_seed(0) logger.info("Total Items: %d" % len(self.market)) methods = ["user_model", "hetero_single_aug", "hetero_multiple_avg", "hetero_ensemble_pruning"] recorders = {method: Recorder() for method in methods} user = self.benchmark.name - + if not skip_test: for idx in range(self.benchmark.user_num): test_x, test_y = self.benchmark.get_test_data(user_ids=idx) test_x, test_y = test_x.values, test_y.values - + train_x, train_y = self.benchmark.get_train_data(user_ids=idx) train_x, train_y, feature_descriptions = train_x.values, train_y.values, train_x.columns train_subsets = self.get_train_subsets(hetero_n_labeled_list, hetero_n_repeat_list, train_x, train_y) - + user_stat_spec = generate_stat_spec(type="table", X=test_x) input_description = { "Dimension": len(feature_descriptions), - "Description": {str(i): feature_descriptions[i] for i in range(len(feature_descriptions))} + "Description": {str(i): feature_descriptions[i] for i in range(len(feature_descriptions))}, } user_semantic["Input"] = input_description - user_info = BaseUserInfo( - semantic_spec=user_semantic, stat_info={user_stat_spec.type: user_stat_spec} - ) - + user_info = BaseUserInfo(semantic_spec=user_semantic, stat_info={user_stat_spec.type: user_stat_spec}) + logger.info(f"Searching Market for user: {user}_{idx}") search_result = self.market.search_learnware(user_info) single_result = search_result.get_single_results() @@ -144,16 +139,25 @@ class HeterogeneousDatasetWorkflow(TableWorkflow): mixture_learnware_list = multiple_result[0].learnwares else: mixture_learnware_list = [single_result[0].learnware] - - logger.info(f"Hetero search result of user {user}_{idx}: mixture learnware num: {len(mixture_learnware_list)}") - test_info = {"user": user, "idx": idx, "train_subsets": train_subsets, "test_x": test_x, "test_y": test_y, "n_labeled_list": hetero_n_labeled_list} + logger.info( + f"Hetero search result of user {user}_{idx}: mixture learnware num: {len(mixture_learnware_list)}" + ) + + test_info = { + "user": user, + "idx": idx, + "train_subsets": train_subsets, + "test_x": test_x, + "test_y": test_y, + "n_labeled_list": hetero_n_labeled_list, + } common_config = {"user_rkme": user_stat_spec, "learnwares": mixture_learnware_list} method_configs = { "user_model": {"dataset": self.benchmark.name, "model_type": "lgb"}, "hetero_single_aug": {"user_rkme": user_stat_spec, "single_learnware": single_result[0].learnware}, "hetero_multiple_avg": common_config, - "hetero_ensemble_pruning": common_config + "hetero_ensemble_pruning": common_config, } for method_name in methods: @@ -161,8 +165,10 @@ class HeterogeneousDatasetWorkflow(TableWorkflow): test_info["method_name"] = method_name test_info.update(method_configs[method_name]) self.test_method(test_info, recorders, loss_func=loss_func_rmse) - + for method, recorder in recorders.items(): recorder.save(os.path.join(self.curves_result_path, f"{user}/{user}_{method}_performance.json")) - - plot_performance_curves(self.curves_result_path, user, recorders, task="Hetero", n_labeled_list=hetero_n_labeled_list) \ No newline at end of file + + plot_performance_curves( + self.curves_result_path, user, recorders, task="Hetero", n_labeled_list=hetero_n_labeled_list + ) diff --git a/examples/dataset_table_workflow/homo.py b/examples/dataset_table_workflow/homo.py index b22f4e0..e23c7be 100644 --- a/examples/dataset_table_workflow/homo.py +++ b/examples/dataset_table_workflow/homo.py @@ -1,18 +1,18 @@ import os import warnings import numpy as np -warnings.filterwarnings("ignore") from learnware.market import BaseUserInfo from learnware.logger import get_module_logger from learnware.specification import generate_stat_spec -from learnware.reuse import AveragingReuser, JobSelectorReuser, EnsemblePruningReuser +from learnware.reuse import AveragingReuser, JobSelectorReuser -from methods import * +from methods import loss_func_rmse from base import TableWorkflow from config import homo_n_labeled_list, homo_n_repeat_list from utils import Recorder, plot_performance_curves +warnings.filterwarnings("ignore") logger = get_module_logger("homo_table", level="INFO") @@ -30,9 +30,7 @@ class HomogeneousDatasetWorkflow(TableWorkflow): test_x, test_y = self.benchmark.get_test_data(user_ids=idx) test_x, test_y = test_x.values, test_y.values user_stat_spec = generate_stat_spec(type="table", X=test_x) - user_info = BaseUserInfo( - semantic_spec=self.user_semantic, stat_info={user_stat_spec.type: user_stat_spec} - ) + user_info = BaseUserInfo(semantic_spec=self.user_semantic, stat_info={user_stat_spec.type: user_stat_spec}) logger.info(f"Searching Market for user: {user}_{idx}") search_result = self.market.search_learnware(user_info, max_search_num=2) @@ -43,7 +41,7 @@ class HomogeneousDatasetWorkflow(TableWorkflow): logger.info( f"single model num: {len(single_result)}, max_score: {single_result[0].score}, min_score: {single_result[-1].score}" ) - + pred_y = single_result[0].learnware.predict(test_x) single_score_list.append(loss_func_rmse(pred_y, test_y)) @@ -77,7 +75,7 @@ class HomogeneousDatasetWorkflow(TableWorkflow): ensemble_score = loss_func_rmse(ensemble_predict_y, test_y) ensemble_score_list.append(ensemble_score) logger.info(f"mixture reuse rmse (ensemble): {ensemble_score}") - + learnware_rmse_list.append(rmse_list) single_list = np.array(learnware_rmse_list) @@ -103,19 +101,18 @@ class HomogeneousDatasetWorkflow(TableWorkflow): "Averaging Ensemble Reuse Performance: %.3f +/- %.3f" % (np.mean(ensemble_score_list), np.std(ensemble_score_list)) ) - - + def labeled_homo_table_example(self, skip_test): logger.info("Total Item: %d" % (len(self.market))) methods = ["user_model", "homo_single_aug", "homo_ensemble_pruning"] recorders = {method: Recorder() for method in methods} user = self.benchmark.name - + if not skip_test: for idx in range(self.benchmark.user_num): test_x, test_y = self.benchmark.get_test_data(user_ids=idx) test_x, test_y = test_x.values, test_y.values - + train_x, train_y = self.benchmark.get_train_data(user_ids=idx) train_x, train_y = train_x.values, train_y.values train_subsets = self.get_train_subsets(homo_n_labeled_list, homo_n_repeat_list, train_x, train_y) @@ -124,7 +121,7 @@ class HomogeneousDatasetWorkflow(TableWorkflow): user_info = BaseUserInfo( semantic_spec=self.user_semantic, stat_info={"RKMETableSpecification": user_stat_spec} ) - + logger.info(f"Searching Market for user: {user}_{idx}") search_result = self.market.search_learnware(user_info) single_result = search_result.get_single_results() @@ -142,12 +139,18 @@ class HomogeneousDatasetWorkflow(TableWorkflow): else: mixture_learnware_list = [single_result[0].learnware] - test_info = {"user": user, "idx": idx, "train_subsets": train_subsets, "test_x": test_x, "test_y": test_y} + test_info = { + "user": user, + "idx": idx, + "train_subsets": train_subsets, + "test_x": test_x, + "test_y": test_y, + } common_config = {"learnwares": mixture_learnware_list} method_configs = { "user_model": {"dataset": self.benchmark.name, "model_type": "lgb"}, "homo_single_aug": {"single_learnware": [single_result[0].learnware]}, - "homo_ensemble_pruning": common_config + "homo_ensemble_pruning": common_config, } for method_name in methods: @@ -155,8 +158,10 @@ class HomogeneousDatasetWorkflow(TableWorkflow): test_info["method_name"] = method_name test_info.update(method_configs[method_name]) self.test_method(test_info, recorders, loss_func=loss_func_rmse) - + for method, recorder in recorders.items(): recorder.save(os.path.join(self.curves_result_path, f"{user}/{user}_{method}_performance.json")) - plot_performance_curves(self.curves_result_path, user, recorders, task="Homo", n_labeled_list=homo_n_labeled_list) \ No newline at end of file + plot_performance_curves( + self.curves_result_path, user, recorders, task="Homo", n_labeled_list=homo_n_labeled_list + ) diff --git a/examples/dataset_table_workflow/methods.py b/examples/dataset_table_workflow/methods.py index fe40eef..b682919 100644 --- a/examples/dataset_table_workflow/methods.py +++ b/examples/dataset_table_workflow/methods.py @@ -31,7 +31,7 @@ class HomoScoringMethods: reuse_multiple_augment = FeatureAugmentReuser(multiple_learnwares, mode="regression") reuse_multiple_augment.fit(x_train=x_train, y_train=y_train) return reuse_multiple_augment - + @staticmethod def multiple_avg_score(x_train, y_train, test_info): multiple_learnwares = test_info["learnwares"] @@ -64,29 +64,35 @@ class HeteroMethods: reuse_single_augment = HeteroMapAlignLearnware(single_learnware, mode="regression", **align_model_params) reuse_single_augment.align(user_rkme=user_rkme, x_train=x_train, y_train=y_train) return reuse_single_augment - + @staticmethod def multiple_aug_score(x_train, y_train, test_info): user_rkme, multiple_learnwares = test_info["user_rkme"], test_info["learnwares"] - hetero_learnware_list = HeteroMethods.create_hetero_learnware_list(multiple_learnwares, user_rkme, x_train, y_train) + hetero_learnware_list = HeteroMethods.create_hetero_learnware_list( + multiple_learnwares, user_rkme, x_train, y_train + ) reuse_multiple_augment = FeatureAugmentReuser(hetero_learnware_list, mode="regression") reuse_multiple_augment.fit(x_train=x_train, y_train=y_train) return reuse_multiple_augment - + @staticmethod def multiple_ensemble_pruning_score(x_train, y_train, test_info): user_rkme, multiple_learnwares = test_info["user_rkme"], test_info["learnwares"] - hetero_learnware_list = HeteroMethods.create_hetero_learnware_list(multiple_learnwares, user_rkme, x_train, y_train) + hetero_learnware_list = HeteroMethods.create_hetero_learnware_list( + multiple_learnwares, user_rkme, x_train, y_train + ) if len(hetero_learnware_list) == 1: return hetero_learnware_list[0] reuse_pruning = EnsemblePruningReuser(hetero_learnware_list, mode="regression") reuse_pruning.fit(val_X=x_train, val_y=y_train) return reuse_pruning - + @staticmethod def multiple_avg_score(x_train, y_train, test_info): user_rkme, multiple_learnwares = test_info["user_rkme"], test_info["learnwares"] - hetero_learnware_list = HeteroMethods.create_hetero_learnware_list(multiple_learnwares, user_rkme, x_train, y_train) + hetero_learnware_list = HeteroMethods.create_hetero_learnware_list( + multiple_learnwares, user_rkme, x_train, y_train + ) reuse_multiple_avg = AveragingReuser(hetero_learnware_list, mode="mean") return reuse_multiple_avg @@ -100,5 +106,5 @@ test_methods = { "homo_single_aug": HomoScoringMethods.single_aug_score, "homo_multiple_aug": HomoScoringMethods.multiple_aug_score, "homo_multiple_avg": HomoScoringMethods.multiple_avg_score, - "homo_ensemble_pruning": HomoScoringMethods.multiple_ensemble_pruning_score -} \ No newline at end of file + "homo_ensemble_pruning": HomoScoringMethods.multiple_ensemble_pruning_score, +} diff --git a/examples/dataset_table_workflow/train.py b/examples/dataset_table_workflow/train.py index b4f1b6c..d8cf66b 100644 --- a/examples/dataset_table_workflow/train.py +++ b/examples/dataset_table_workflow/train.py @@ -1,7 +1,5 @@ -import numpy as np import lightgbm as lgb from lightgbm import early_stopping -from sklearn.metrics import mean_squared_error from learnware.logger import get_module_logger from config import user_model_params @@ -24,7 +22,7 @@ def train_lgb(X_train, y_train, X_val, y_val, dataset): dtrain, num_boost_round=MAX_ROUNDS, valid_sets=[dtrain, dval] if dataset == "Corporacion" else [dval], - callbacks=[early_stopping(model_param["early_stopping_rounds"], verbose=False)] + callbacks=[early_stopping(model_param["early_stopping_rounds"], verbose=False)], ) val_pred.append(bst.predict(X_val, num_iteration=bst.best_iteration or MAX_ROUNDS)) return bst @@ -38,6 +36,6 @@ def train_model(X_train, y_train, X_val, y_val, test_info): dataset = test_info["dataset"] model_type = test_info["model_type"] assert model_type in ["lgb", "ridge"] - + if model_type == "lgb": - return train_lgb(X_train, y_train, X_val, y_val, dataset) \ No newline at end of file + return train_lgb(X_train, y_train, X_val, y_val, dataset) diff --git a/examples/dataset_table_workflow/utils.py b/examples/dataset_table_workflow/utils.py index f176a6a..08e21e6 100644 --- a/examples/dataset_table_workflow/utils.py +++ b/examples/dataset_table_workflow/utils.py @@ -8,7 +8,7 @@ import matplotlib.pyplot as plt from collections import defaultdict from learnware.logger import get_module_logger -from config import * +from config import styles, labels logger = get_module_logger("base_table", level="INFO") @@ -29,46 +29,46 @@ class Recorder: def save(self, path): with open(path, "w") as f: json.dump(self.data, f, indent=4, default=list) - + def load(self, path): with open(path, "r") as f: self.data = json.load(f, object_hook=lambda x: defaultdict(list, x)) - + def should_test_method(self, user, idx, path): if os.path.exists(path): self.load(path) return user not in self.data or idx > len(self.data[user]) - 1 return True - + def plot_performance_curves(path, user, recorders, task, n_labeled_list): plt.figure(figsize=(10, 6)) plt.xticks(range(len(n_labeled_list)), n_labeled_list) - for method, recorder in recorders.items(): + for method, recorder in recorders.items(): data_path = os.path.join(path, f"{user}/{user}_{method}_performance.json") recorder.load(data_path) scores_array = recorder.get_performance_data(user) - + mean_curve, std_curve = [], [] for i in range(len(n_labeled_list)): sub_scores_array = np.vstack([lst[i] for lst in scores_array]) - sub_scores_mean = np.squeeze(np.mean(sub_scores_array, axis=0)) + sub_scores_mean = np.squeeze(np.mean(sub_scores_array, axis=0)) mean_curve.append(np.mean(sub_scores_mean)) std_curve.append(np.std(sub_scores_mean)) - + mean_curve = np.array(mean_curve) std_curve = np.array(std_curve) - method_plot = '_'.join(method.split('_')[1:]) if method not in ['user_model', 'oracle_score', 'select_score', 'mean_score'] else method + method_plot = ( + "_".join(method.split("_")[1:]) + if method not in ["user_model", "oracle_score", "select_score", "mean_score"] + else method + ) style = styles.get(method_plot, {"color": "black", "linestyle": "-"}) plt.plot(mean_curve, label=labels.get(method_plot), **style) plt.fill_between( - range(len(mean_curve)), - mean_curve - std_curve, - mean_curve + std_curve, - color=style["color"], - alpha=0.2 + range(len(mean_curve)), mean_curve - std_curve, mean_curve + std_curve, color=style["color"], alpha=0.2 ) plt.xlabel("Amount of Labeled User Data", fontsize=14) @@ -76,7 +76,7 @@ def plot_performance_curves(path, user, recorders, task, n_labeled_list): plt.title(f"Results on {task} Table Experimental Scenario", fontsize=16) plt.legend(fontsize=12) plt.tight_layout() - + root_path = os.path.abspath(os.path.join(__file__, "..")) fig_path = os.path.join(root_path, "results", "figs") os.makedirs(fig_path, exist_ok=True) @@ -91,4 +91,4 @@ def set_seed(seed): torch.cuda.manual_seed(seed) torch.cuda.manual_seed_all(seed) torch.backends.cudnn.benchmark = False - torch.backends.cudnn.deterministic = True \ No newline at end of file + torch.backends.cudnn.deterministic = True From 584b73e318cf9ad9361b3068a7f0215732d5a536 Mon Sep 17 00:00:00 2001 From: Gene Date: Mon, 15 Jan 2024 11:24:59 +0800 Subject: [PATCH 30/32] [DOC] add the readme for table example --- examples/dataset_table_workflow/README.md | 94 +++++++++++++++++++++++ 1 file changed, 94 insertions(+) diff --git a/examples/dataset_table_workflow/README.md b/examples/dataset_table_workflow/README.md index e69de29..a9f6677 100644 --- a/examples/dataset_table_workflow/README.md +++ b/examples/dataset_table_workflow/README.md @@ -0,0 +1,94 @@ +# Tabular Dataset Workflow Example + +## Introduction + +On various tabular datasets, we initially evaluate the performance of identifying and reusing learnwares from the learnware market that share the same feature space as the user's tasks. Additionally, since tabular tasks often come from heterogeneous feature spaces, we also assess the identification and reuse of learnwares from different feature spaces. + +### Settings + +Our study utilize three public datasets in the field of sales forecasting: [Predict Future Sales (PFS)](https://www.kaggle.com/c/competitive-data-science-predict-future-sales/data), [M5 Forecasting (M5)](https://www.kaggle.com/competitions/m5-forecasting-accuracy/data), and [Corporacion](https://www.kaggle.com/competitions/favorita-grocery-sales-forecasting/data). To enrich the data, we apply diverse feature engineering methods to these datasets. Then we divide each dataset by store and further split the data for each store into training and test sets. A LightGBM is trained on each Corporacion and PFS training set, while the test sets and M5 datasets are reversed to construct user tasks. This results in an experimental market consisting of 265 learnwares, encompassing five types of feature spaces and two types of label spaces. All these learnwares have been uploaded to the [Beimingwu system](https://bmwu.cloud/). + +### Baseline algorithms +The most basic way to reuse a learnware is Top-1 reuser, which directly uses the single learnware chosen by RKME specification. Besides, we implement two data-free reusers and two data-dependent reusers that works on single or multiple helpful learnwares identified from the market. When users have no labeled data, JobSelector reuser selects different learnwares for different samples by training a job selector classifier; AverageEnsemble reuser uses an ensemble method to make predictions. In cases where users possess both test data and limited labeled training data, EnsemblePruning reuser selectively ensembles a subset of learnwares to choose the ones that are most suitable for the user’s task; FeatureAugment reuser regards each received learnware as a feature augmentor, taking its output as a new feature and then builds a simple model on the augmented feature set. JobSelector and FeatureAugment are only effective for tabular data, while others are also useful for text and image data. + +## Homogeneous Cases + +In the homogeneous cases, the 53 stores within the PFS dataset function as 53 individual users. Each store utilizes its own test data as user data and applies the same feature engineering approach used in the learnware market. These users could subsequently search for homogeneous learnwares within the market that possessed the same feature spaces as their tasks. + +We conduct a comparison among different baseline algorithms when the users have no labeled data or limited amounts of labeled data. The average losses over all users are illustrated in the table below. It shows that unlabeled methods are much better than random choosing and deploying one learnware from the market. + +

+ +The figure below showcases the results for different amounts of labeled data provided by the user; for each user, we conducted multiple experiments repeatedly and calculated the mean and standard deviation of the losses; the average losses over all users are illustrated in the figure. It illustrates that when users have limited training data, identifying and reusing single or multiple learnwares yields superior performance compared to user's self-trained models. + +
+ +
+ +## Heterogeneous Cases + +Based on the similarity of tasks between the market's learnwares and the users, the heterogeneous cases can be further categorized into different feature engineering and different task scenarios. + +### Different Feature Engineering Scenarios + +We consider the 41 stores within the PFS dataset as users, generating their user data using a unique feature engineering approach that differ from the methods employed by the learnwares in the market. As a result, while some learnwares in the market are also designed for the PFS dataset, the feature spaces do not align exactly. + +In this experimental setup, we examine various data-free reusers. The results in the following table indicate that even when users lack labeled data, the market exhibits strong performance, particularly with the AverageEnsemble method that reuses multiple learnwares. + +
+ +| Setting | MSE | +|-----------------------------------|--------| +| Mean in Market (Single) | 1.149 | +| Best in Market (Single) | 1.038 | +| Top-1 Reuse (Single) | 1.105 | +| Average Ensemble Reuse (Multiple) | 1.081 | + +
+ + +### Different Task Scenarios + +We employ three distinct feature engineering methods on all the ten stores from the M5 dataset, resulting in a total of 30 users. Although the overall task of sales forecasting aligns with the tasks addressed by the learnwares in the market, there are no learnwares specifically designed to satisfy the M5 sales forecasting requirements. + +In the following figure, we present the loss curves for the user's self-trained model and several learnware reuse methods. It is evident that heterogeneous learnwares prove beneficial with a limited amount of the user's labeled data, facilitating better alignment with the user's specific task. + +
+ +
+ +## Reproduction + +Run the following command to get the table results in `Homogeneous Cases`: + +```bash +python workflow.py unlabeled_homo_table_example +``` + +Run the following command to get the figure results in `Homogeneous Cases`: + +```bash +python workflow.py labeled_homo_table_example +``` + +Run the following command to get the table results in `Heterogeneous Cases`: + +```bash +python workflow.py cross_feat_eng_hetero_table_example +``` + +Run the following command to get the figure results in `Heterogeneous Cases`: + +```bash +python workflow.py cross_task_hetero_table_example +``` \ No newline at end of file From be1f1b0bc668f7d9bfcdc4a36d7ff63eceea5a9c Mon Sep 17 00:00:00 2001 From: Gene Date: Mon, 15 Jan 2024 11:28:40 +0800 Subject: [PATCH 31/32] [DOC] add the dependency in readme --- examples/dataset_table_workflow/README.md | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/examples/dataset_table_workflow/README.md b/examples/dataset_table_workflow/README.md index a9f6677..83094f5 100644 --- a/examples/dataset_table_workflow/README.md +++ b/examples/dataset_table_workflow/README.md @@ -69,6 +69,16 @@ In the following figure, we present the loss curves for the user's self-trained ## Reproduction +### Installation + +To reproduce the above experiment, you need to install the necessary dependencies on top of the environment of `learnware` package. The specific commands are as follows: + +```bash +python -m pip install -r requirements.txt +``` + +### Run the code + Run the following command to get the table results in `Homogeneous Cases`: ```bash From a09b619d828cfe1647f242cbd679e52c47660f59 Mon Sep 17 00:00:00 2001 From: bxdd Date: Mon, 15 Jan 2024 23:47:13 +0800 Subject: [PATCH 32/32] [MNT] unify the code format --- docs/_static/img/table_homo_labeled.png | Bin 729641 -> 0 bytes examples/__init__.py | 0 examples/dataset_image_workflow/config.py | 1 - examples/dataset_image_workflow/utils.py | 4 ++-- examples/dataset_image_workflow/workflow.py | 21 +++++++++--------- examples/dataset_table_workflow/base.py | 13 ++++++----- examples/dataset_table_workflow/config.py | 1 - examples/dataset_table_workflow/hetero.py | 12 +++++----- examples/dataset_table_workflow/homo.py | 14 ++++++------ examples/dataset_table_workflow/methods.py | 4 ++-- examples/dataset_table_workflow/train.py | 2 +- examples/dataset_table_workflow/utils.py | 10 ++++----- examples/dataset_table_workflow/workflow.py | 10 ++++----- examples/dataset_text_workflow/config.py | 1 - examples/dataset_text_workflow/workflow.py | 17 +++++++------- learnware/tests/benchmarks/__init__.py | 3 ++- learnware/tests/benchmarks/config.py | 2 +- setup.py | 1 + tests/test_function/test_search.py | 8 +++---- .../test_all_learnware.py | 6 ++--- .../test_check_learnware.py | 2 +- tests/test_learnware_client/test_container.py | 1 + .../test_load_learnware.py | 1 + tests/test_learnware_client/test_upload.py | 4 ++-- tests/test_specification/test_hetero_spec.py | 8 +++---- tests/test_specification/test_image_rkme.py | 10 ++++----- tests/test_specification/test_table_rkme.py | 8 +++---- tests/test_specification/test_text_rkme.py | 9 ++++---- tests/test_workflow/test_hetero_workflow.py | 18 +++++++-------- tests/test_workflow/test_workflow.py | 11 ++++----- 30 files changed, 103 insertions(+), 99 deletions(-) delete mode 100644 docs/_static/img/table_homo_labeled.png 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B0jK}~ diff --git a/examples/__init__.py b/examples/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/examples/dataset_image_workflow/config.py b/examples/dataset_image_workflow/config.py index ea199a5..688e7cd 100644 --- a/examples/dataset_image_workflow/config.py +++ b/examples/dataset_image_workflow/config.py @@ -1,6 +1,5 @@ from learnware.tests.benchmarks import BenchmarkConfig - image_benchmark_config = BenchmarkConfig( name="CIFAR-10", user_num=100, diff --git a/examples/dataset_image_workflow/utils.py b/examples/dataset_image_workflow/utils.py index 1f7f209..7611503 100644 --- a/examples/dataset_image_workflow/utils.py +++ b/examples/dataset_image_workflow/utils.py @@ -1,6 +1,6 @@ -import torch import numpy as np -from torch import optim, nn +import torch +from torch import nn, optim from torch.utils.data import DataLoader, Dataset from learnware.utils import choose_device diff --git a/examples/dataset_image_workflow/workflow.py b/examples/dataset_image_workflow/workflow.py index b099f0f..685a47f 100644 --- a/examples/dataset_image_workflow/workflow.py +++ b/examples/dataset_image_workflow/workflow.py @@ -1,24 +1,25 @@ import os -import fire -import time -import torch import pickle import random import tempfile -import numpy as np +import time + +import fire import matplotlib.pyplot as plt +import numpy as np +import torch +from config import image_benchmark_config +from model import ConvModel from torch.utils.data import TensorDataset +from utils import evaluate, train_model -from learnware.utils import choose_device from learnware.client import LearnwareClient from learnware.logger import get_module_logger +from learnware.market import BaseUserInfo, instantiate_learnware_market +from learnware.reuse import AveragingReuser, EnsemblePruningReuser, JobSelectorReuser from learnware.specification import generate_stat_spec from learnware.tests.benchmarks import LearnwareBenchmark -from learnware.market import instantiate_learnware_market, BaseUserInfo -from learnware.reuse import JobSelectorReuser, AveragingReuser, EnsemblePruningReuser -from model import ConvModel -from utils import train_model, evaluate -from config import image_benchmark_config +from learnware.utils import choose_device logger = get_module_logger("image_workflow", level="INFO") diff --git a/examples/dataset_table_workflow/base.py b/examples/dataset_table_workflow/base.py index 7e619ef..6f6559c 100644 --- a/examples/dataset_table_workflow/base.py +++ b/examples/dataset_table_workflow/base.py @@ -1,20 +1,21 @@ import os -import time import random -import requests import tempfile +import time import traceback + import numpy as np +import requests +from config import market_mapping_params +from methods import loss_func_rmse, test_methods +from utils import set_seed + from learnware.client import LearnwareClient from learnware.logger import get_module_logger from learnware.market import instantiate_learnware_market from learnware.reuse.utils import fill_data_with_mean from learnware.tests.benchmarks import LearnwareBenchmark -from config import market_mapping_params -from methods import loss_func_rmse, test_methods -from utils import set_seed - logger = get_module_logger("base_table", level="INFO") diff --git a/examples/dataset_table_workflow/config.py b/examples/dataset_table_workflow/config.py index 5c27d33..5f835c4 100644 --- a/examples/dataset_table_workflow/config.py +++ b/examples/dataset_table_workflow/config.py @@ -1,6 +1,5 @@ from learnware.tests.benchmarks import BenchmarkConfig - homo_n_labeled_list = [100, 200, 500, 1000, 2000, 4000, 6000, 8000, 10000] homo_n_repeat_list = [10, 10, 10, 3, 3, 3, 3, 3, 3] hetero_n_labeled_list = [10, 30, 50, 75, 100, 200, 500, 1000, 2000] diff --git a/examples/dataset_table_workflow/hetero.py b/examples/dataset_table_workflow/hetero.py index 423f7b9..4d0e173 100644 --- a/examples/dataset_table_workflow/hetero.py +++ b/examples/dataset_table_workflow/hetero.py @@ -2,15 +2,15 @@ import os import warnings import numpy as np +from base import TableWorkflow +from config import align_model_params, hetero_n_labeled_list, hetero_n_repeat_list, user_semantic +from methods import loss_func_rmse +from utils import Recorder, plot_performance_curves, set_seed + from learnware.logger import get_module_logger -from learnware.specification import generate_stat_spec from learnware.market import BaseUserInfo from learnware.reuse import AveragingReuser, FeatureAlignLearnware - -from methods import loss_func_rmse -from base import TableWorkflow -from config import align_model_params, user_semantic, hetero_n_labeled_list, hetero_n_repeat_list -from utils import Recorder, plot_performance_curves, set_seed +from learnware.specification import generate_stat_spec warnings.filterwarnings("ignore") logger = get_module_logger("hetero_test", level="INFO") diff --git a/examples/dataset_table_workflow/homo.py b/examples/dataset_table_workflow/homo.py index e23c7be..b13ca2f 100644 --- a/examples/dataset_table_workflow/homo.py +++ b/examples/dataset_table_workflow/homo.py @@ -1,17 +1,17 @@ import os import warnings -import numpy as np -from learnware.market import BaseUserInfo -from learnware.logger import get_module_logger -from learnware.specification import generate_stat_spec -from learnware.reuse import AveragingReuser, JobSelectorReuser - -from methods import loss_func_rmse +import numpy as np from base import TableWorkflow from config import homo_n_labeled_list, homo_n_repeat_list +from methods import loss_func_rmse from utils import Recorder, plot_performance_curves +from learnware.logger import get_module_logger +from learnware.market import BaseUserInfo +from learnware.reuse import AveragingReuser, JobSelectorReuser +from learnware.specification import generate_stat_spec + warnings.filterwarnings("ignore") logger = get_module_logger("homo_table", level="INFO") diff --git a/examples/dataset_table_workflow/methods.py b/examples/dataset_table_workflow/methods.py index b682919..34cb056 100644 --- a/examples/dataset_table_workflow/methods.py +++ b/examples/dataset_table_workflow/methods.py @@ -1,10 +1,10 @@ import numpy as np +from config import align_model_params from sklearn.metrics import mean_squared_error from sklearn.model_selection import train_test_split +from train import train_model from learnware.reuse import AveragingReuser, EnsemblePruningReuser, FeatureAugmentReuser, HeteroMapAlignLearnware -from config import align_model_params -from train import train_model def loss_func_rmse(y_true, y_pred): diff --git a/examples/dataset_table_workflow/train.py b/examples/dataset_table_workflow/train.py index d8cf66b..3b841b2 100644 --- a/examples/dataset_table_workflow/train.py +++ b/examples/dataset_table_workflow/train.py @@ -1,8 +1,8 @@ import lightgbm as lgb +from config import user_model_params from lightgbm import early_stopping from learnware.logger import get_module_logger -from config import user_model_params logger = get_module_logger("train_table", level="INFO") diff --git a/examples/dataset_table_workflow/utils.py b/examples/dataset_table_workflow/utils.py index 08e21e6..12285d0 100644 --- a/examples/dataset_table_workflow/utils.py +++ b/examples/dataset_table_workflow/utils.py @@ -1,14 +1,14 @@ -import os import json +import os import random +from collections import defaultdict -import torch -import numpy as np import matplotlib.pyplot as plt -from collections import defaultdict +import numpy as np +import torch +from config import labels, styles from learnware.logger import get_module_logger -from config import styles, labels logger = get_module_logger("base_table", level="INFO") diff --git a/examples/dataset_table_workflow/workflow.py b/examples/dataset_table_workflow/workflow.py index 9352e2a..4265285 100644 --- a/examples/dataset_table_workflow/workflow.py +++ b/examples/dataset_table_workflow/workflow.py @@ -1,13 +1,13 @@ import fire - -from learnware.logger import get_module_logger -from homo import HomogeneousDatasetWorkflow -from hetero import HeterogeneousDatasetWorkflow from config import ( - homo_table_benchmark_config, hetero_cross_feat_eng_benchmark_config, hetero_cross_task_benchmark_config, + homo_table_benchmark_config, ) +from hetero import HeterogeneousDatasetWorkflow +from homo import HomogeneousDatasetWorkflow + +from learnware.logger import get_module_logger logger = get_module_logger("base_table", level="INFO") diff --git a/examples/dataset_text_workflow/config.py b/examples/dataset_text_workflow/config.py index d44f629..d5cee62 100644 --- a/examples/dataset_text_workflow/config.py +++ b/examples/dataset_text_workflow/config.py @@ -1,6 +1,5 @@ from learnware.tests.benchmarks import BenchmarkConfig - text_benchmark_config = BenchmarkConfig( name="20-Newsgroups", user_num=10, diff --git a/examples/dataset_text_workflow/workflow.py b/examples/dataset_text_workflow/workflow.py index f425fdb..42bc315 100644 --- a/examples/dataset_text_workflow/workflow.py +++ b/examples/dataset_text_workflow/workflow.py @@ -1,22 +1,23 @@ import os -import fire -import time -import random import pickle +import random import tempfile -import numpy as np +import time + +import fire import matplotlib.pyplot as plt +import numpy as np +from config import text_benchmark_config +from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics import accuracy_score from sklearn.naive_bayes import MultinomialNB -from sklearn.feature_extraction.text import TfidfVectorizer from learnware.client import LearnwareClient from learnware.logger import get_module_logger +from learnware.market import BaseUserInfo, instantiate_learnware_market +from learnware.reuse import AveragingReuser, EnsemblePruningReuser, JobSelectorReuser from learnware.specification import RKMETextSpecification from learnware.tests.benchmarks import LearnwareBenchmark -from learnware.market import instantiate_learnware_market, BaseUserInfo -from learnware.reuse import JobSelectorReuser, AveragingReuser, EnsemblePruningReuser -from config import text_benchmark_config logger = get_module_logger("text_workflow", level="INFO") diff --git a/learnware/tests/benchmarks/__init__.py b/learnware/tests/benchmarks/__init__.py index 05609f9..436d5ae 100644 --- a/learnware/tests/benchmarks/__init__.py +++ b/learnware/tests/benchmarks/__init__.py @@ -2,10 +2,11 @@ import os import pickle import tempfile import zipfile -import numpy as np from dataclasses import dataclass from typing import List, Optional, Tuple, Union +import numpy as np + from .config import BenchmarkConfig, benchmark_configs from ..data import GetData from ...config import C diff --git a/learnware/tests/benchmarks/config.py b/learnware/tests/benchmarks/config.py index 24a8dfd..3921900 100644 --- a/learnware/tests/benchmarks/config.py +++ b/learnware/tests/benchmarks/config.py @@ -1,5 +1,5 @@ from dataclasses import dataclass -from typing import List, Optional, Dict +from typing import Dict, List, Optional @dataclass diff --git a/setup.py b/setup.py index 574a3c4..2178209 100644 --- a/setup.py +++ b/setup.py @@ -1,4 +1,5 @@ import os + from setuptools import find_packages, setup diff --git a/tests/test_function/test_search.py b/tests/test_function/test_search.py index 995e3bc..c376f21 100644 --- a/tests/test_function/test_search.py +++ b/tests/test_function/test_search.py @@ -1,12 +1,12 @@ +import logging import os -import unittest import tempfile -import logging +import unittest import learnware -from learnware.learnware import Learnware from learnware.client import LearnwareClient -from learnware.market import instantiate_learnware_market, BaseUserInfo +from learnware.learnware import Learnware +from learnware.market import BaseUserInfo, instantiate_learnware_market learnware.init(logging_level=logging.WARNING) diff --git a/tests/test_learnware_client/test_all_learnware.py b/tests/test_learnware_client/test_all_learnware.py index dff378e..8a3a5b0 100644 --- a/tests/test_learnware_client/test_all_learnware.py +++ b/tests/test_learnware_client/test_all_learnware.py @@ -1,11 +1,11 @@ -import os import json -import unittest +import os import tempfile +import unittest from learnware.client import LearnwareClient -from learnware.specification import generate_semantic_spec from learnware.market import BaseUserInfo +from learnware.specification import generate_semantic_spec class TestAllLearnware(unittest.TestCase): diff --git a/tests/test_learnware_client/test_check_learnware.py b/tests/test_learnware_client/test_check_learnware.py index b831830..c4e7dd3 100644 --- a/tests/test_learnware_client/test_check_learnware.py +++ b/tests/test_learnware_client/test_check_learnware.py @@ -1,6 +1,6 @@ import os -import unittest import tempfile +import unittest from learnware.client import LearnwareClient diff --git a/tests/test_learnware_client/test_container.py b/tests/test_learnware_client/test_container.py index 4e1f1f4..3aab015 100644 --- a/tests/test_learnware_client/test_container.py +++ b/tests/test_learnware_client/test_container.py @@ -1,4 +1,5 @@ import unittest + import numpy as np from learnware.client import LearnwareClient diff --git a/tests/test_learnware_client/test_load_learnware.py b/tests/test_learnware_client/test_load_learnware.py index 1ce2250..12f65b4 100644 --- a/tests/test_learnware_client/test_load_learnware.py +++ b/tests/test_learnware_client/test_load_learnware.py @@ -1,5 +1,6 @@ import os import unittest + import numpy as np from learnware.client import LearnwareClient diff --git a/tests/test_learnware_client/test_upload.py b/tests/test_learnware_client/test_upload.py index 4a8c17a..c44d532 100644 --- a/tests/test_learnware_client/test_upload.py +++ b/tests/test_learnware_client/test_upload.py @@ -1,7 +1,7 @@ -import os import json -import unittest +import os import tempfile +import unittest from learnware.client import LearnwareClient from learnware.specification import generate_semantic_spec diff --git a/tests/test_specification/test_hetero_spec.py b/tests/test_specification/test_hetero_spec.py index b0600d7..e4482ee 100644 --- a/tests/test_specification/test_hetero_spec.py +++ b/tests/test_specification/test_hetero_spec.py @@ -1,12 +1,12 @@ -import os import json -import unittest +import os import tempfile +import unittest + import numpy as np -from learnware.specification import RKMETableSpecification, HeteroMapTableSpecification -from learnware.specification import generate_stat_spec from learnware.market.heterogeneous.organizer import HeteroMap +from learnware.specification import HeteroMapTableSpecification, RKMETableSpecification, generate_stat_spec class TestTableRKME(unittest.TestCase): diff --git a/tests/test_specification/test_image_rkme.py b/tests/test_specification/test_image_rkme.py index 4bd71b5..5ddc295 100644 --- a/tests/test_specification/test_image_rkme.py +++ b/tests/test_specification/test_image_rkme.py @@ -1,12 +1,12 @@ -import os import json -import torch -import unittest +import os import tempfile +import unittest + import numpy as np +import torch -from learnware.specification import RKMEImageSpecification -from learnware.specification import generate_stat_spec +from learnware.specification import RKMEImageSpecification, generate_stat_spec class TestImageRKME(unittest.TestCase): diff --git a/tests/test_specification/test_table_rkme.py b/tests/test_specification/test_table_rkme.py index e57e8d0..75f2b27 100644 --- a/tests/test_specification/test_table_rkme.py +++ b/tests/test_specification/test_table_rkme.py @@ -1,11 +1,11 @@ -import os import json -import unittest +import os import tempfile +import unittest + import numpy as np -from learnware.specification import RKMETableSpecification -from learnware.specification import generate_stat_spec +from learnware.specification import RKMETableSpecification, generate_stat_spec class TestTableRKME(unittest.TestCase): diff --git a/tests/test_specification/test_text_rkme.py b/tests/test_specification/test_text_rkme.py index 6675cf4..ca00077 100644 --- a/tests/test_specification/test_text_rkme.py +++ b/tests/test_specification/test_text_rkme.py @@ -1,12 +1,11 @@ -import os import json -import string +import os import random -import unittest +import string import tempfile +import unittest -from learnware.specification import RKMETextSpecification -from learnware.specification import generate_stat_spec +from learnware.specification import RKMETextSpecification, generate_stat_spec class TestTextRKME(unittest.TestCase): diff --git a/tests/test_workflow/test_hetero_workflow.py b/tests/test_workflow/test_hetero_workflow.py index fb44380..0152b69 100644 --- a/tests/test_workflow/test_hetero_workflow.py +++ b/tests/test_workflow/test_hetero_workflow.py @@ -1,22 +1,22 @@ -import torch -import pickle -import unittest -import os import logging +import os +import pickle import tempfile +import unittest import zipfile -from sklearn.linear_model import Ridge + +import torch +from hetero_config import input_description_list, input_shape_list, output_description_list, user_description_list from sklearn.datasets import make_regression +from sklearn.linear_model import Ridge from sklearn.metrics import mean_squared_error import learnware -from learnware.market import instantiate_learnware_market, BaseUserInfo +from learnware.market import BaseUserInfo, instantiate_learnware_market +from learnware.reuse import AveragingReuser, EnsemblePruningReuser, HeteroMapAlignLearnware from learnware.specification import RKMETableSpecification, generate_rkme_table_spec, generate_semantic_spec -from learnware.reuse import HeteroMapAlignLearnware, AveragingReuser, EnsemblePruningReuser from learnware.tests.templates import LearnwareTemplate, PickleModelTemplate, StatSpecTemplate -from hetero_config import input_shape_list, input_description_list, output_description_list, user_description_list - learnware.init(logging_level=logging.WARNING) curr_root = os.path.dirname(os.path.abspath(__file__)) diff --git a/tests/test_workflow/test_workflow.py b/tests/test_workflow/test_workflow.py index 31656fe..35ab9bf 100644 --- a/tests/test_workflow/test_workflow.py +++ b/tests/test_workflow/test_workflow.py @@ -1,18 +1,19 @@ -import unittest -import os import logging -import tempfile +import os import pickle +import tempfile +import unittest import zipfile + import numpy as np from sklearn import svm from sklearn.datasets import load_digits from sklearn.model_selection import train_test_split import learnware -from learnware.market import instantiate_learnware_market, BaseUserInfo +from learnware.market import BaseUserInfo, instantiate_learnware_market +from learnware.reuse import AveragingReuser, EnsemblePruningReuser, FeatureAugmentReuser, JobSelectorReuser from learnware.specification import RKMETableSpecification, generate_rkme_table_spec, generate_semantic_spec -from learnware.reuse import JobSelectorReuser, AveragingReuser, EnsemblePruningReuser, FeatureAugmentReuser from learnware.tests.templates import LearnwareTemplate, PickleModelTemplate, StatSpecTemplate learnware.init(logging_level=logging.WARNING)