learnware/examples/workflow_by_code/main.py

import os
import fire
import joblib
import zipfile
import numpy as np
from sklearn import svm
from shutil import copyfile, rmtree

import learnware
from learnware.market import EasyMarket, BaseUserInfo
from learnware.market import database_ops
from learnware.learnware import Learnware
import learnware.specification as specification
from learnware.utils import get_module_by_module_path

curr_root = os.path.dirname(os.path.abspath(__file__))

semantic_specs = [
    {
        "Data": {"Values": ["Tabular"], "Type": "Class"},
        "Task": {
            "Values": ["Classification"],
            "Type": "Class",
        },
        "Device": {"Values": ["GPU"], "Type": "Tag"},
        "Scenario": {"Values": ["Nature"], "Type": "Tag"},
        "Description": {"Values": "", "Type": "Description"},
        "Name": {"Values": "learnware_1", "Type": "Name"},
    },
    {
        "Data": {"Values": ["Tabular"], "Type": "Class"},
        "Task": {
            "Values": ["Classification"],
            "Type": "Class",
        },
        "Device": {"Values": ["GPU"], "Type": "Tag"},
        "Scenario": {"Values": ["Business", "Nature"], "Type": "Tag"},
        "Description": {"Values": "", "Type": "Description"},
        "Name": {"Values": "learnware_2", "Type": "Name"},
    },
    {
        "Data": {"Values": ["Tabular"], "Type": "Class"},
        "Task": {
            "Values": ["Classification"],
            "Type": "Class",
        },
        "Device": {"Values": ["GPU"], "Type": "Tag"},
        "Scenario": {"Values": ["Business"], "Type": "Tag"},
        "Description": {"Values": "", "Type": "Description"},
        "Name": {"Values": "learnware_3", "Type": "Name"},
    },
]

user_senmantic = {
    "Data": {"Values": ["Tabular"], "Type": "Class"},
    "Task": {
        "Values": ["Classification"],
        "Type": "Class",
    },
    "Device": {"Values": ["GPU"], "Type": "Tag"},
    "Scenario": {"Values": ["Business"], "Type": "Tag"},
    "Description": {"Values": "", "Type": "Description"},
    "Name": {"Values": "", "Type": "Name"},
}


class LearnwareMarketWorkflow:
    def _init_learnware_market(self):
        """initialize learnware market"""
        database_ops.clear_learnware_table()
        learnware.init()
        np.random.seed(2023)

    def prepare_learnware_randomly(self, learnware_num=10):
        self.zip_path_list = []
        for i in range(learnware_num):
            dir_path = os.path.join(curr_root, "learnware_pool", "svm_%d" % (i))
            os.makedirs(dir_path, exist_ok=True)

            print("Preparing Learnware: %d" % (i))
            data_X = np.random.randn(5000, 20) * i
            data_y = np.random.randn(5000)
            data_y = np.where(data_y > 0, 1, 0)

            clf = svm.SVC(kernel="linear")
            clf.fit(data_X, data_y)
            joblib.dump(clf, os.path.join(dir_path, "svm.pkl"))

            spec = specification.utils.generate_rkme_spec(X=data_X, gamma=0.1, cuda_idx=0)
            spec.save(os.path.join(dir_path, "svm.json"))

            init_file = os.path.join(dir_path, "__init__.py")
            copyfile("example_init.py", init_file)  # cp example_init.py init_file

            yaml_file = os.path.join(dir_path, "learnware.yaml")
            copyfile("example.yaml", yaml_file)  # cp example.yaml yaml_file

            zip_file = dir_path + ".zip"
            # zip -q -r -j zip_file dir_path
            with zipfile.ZipFile(zip_file, "w") as zip_obj:
                for foldername, subfolders, 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

            self.zip_path_list.append(zip_file)

    def test_upload_delete_learnware(self, learnware_num=5, delete=False):
        self._init_learnware_market()
        self.prepare_learnware_randomly(learnware_num)

        easy_market = EasyMarket()
        print("Total Item:", len(easy_market))

        for idx, zip_path in enumerate(self.zip_path_list):
            semantic_spec = semantic_specs[idx % 3]
            semantic_spec["Name"]["Values"] = "learnware_%d" % (idx)
            semantic_spec["Description"]["Values"] = "test_learnware_number_%d" % (idx)
            easy_market.add_learnware(zip_path, semantic_spec)

        print("Total Item:", len(easy_market))
        curr_inds = easy_market._get_ids()
        print("Available ids After Uploading Learnwares:", curr_inds)

        if delete:
            for learnware_id in curr_inds:
                easy_market.delete_learnware(learnware_id)
                easy_market.delete_learnware(learnware_id)
            curr_inds = easy_market._get_ids()
            print("Available ids After Deleting Learnwares:", curr_inds)

        return easy_market

    def test_search_semantics(self, learnware_num=5):
        easy_market = self.test_upload_delete_learnware(learnware_num, delete=False)
        print("Total Item:", len(easy_market))

        test_folder = os.path.join(curr_root, "test_semantics")

        idx, zip_path = 1, self.zip_path_list[1]
        unzip_dir = os.path.join(test_folder, f"{idx}")

        # unzip -o -q zip_path -d unzip_dir
        if os.path.exists(unzip_dir):
            rmtree(unzip_dir)
        os.makedirs(unzip_dir, exist_ok=True)

        with zipfile.ZipFile(zip_path, "r") as zip_obj:
            zip_obj.extractall(path=unzip_dir)

        user_info = BaseUserInfo(id="user_0", semantic_spec=user_senmantic)
        _, single_learnware_list, _ = easy_market.search_learnware(user_info)

        print("User info:", user_info.get_semantic_spec())
        print(f"search result of user{idx}:")
        for learnware in single_learnware_list:
            print("Choose learnware:", learnware.id, learnware.get_specification().get_semantic_spec())

        rmtree(test_folder)  # rm -r test_folder

    def test_stat_search(self, learnware_num=5):
        easy_market = self.test_upload_delete_learnware(learnware_num, delete=False)
        print("Total Item:", len(easy_market))

        test_folder = os.path.join(curr_root, "test_stat")

        for idx, zip_path in enumerate(self.zip_path_list):
            unzip_dir = os.path.join(test_folder, f"{idx}")

            # unzip -o -q zip_path -d unzip_dir
            if os.path.exists(unzip_dir):
                rmtree(unzip_dir)
            os.makedirs(unzip_dir, exist_ok=True)
            with zipfile.ZipFile(zip_path, "r") as zip_obj:
                zip_obj.extractall(path=unzip_dir)

            user_spec = specification.rkme.RKMEStatSpecification()
            user_spec.load(os.path.join(unzip_dir, "svm.json"))
            user_info = BaseUserInfo(
                id="user_0", semantic_spec=user_senmantic, stat_info={"RKMEStatSpecification": user_spec}
            )
            sorted_score_list, single_learnware_list, mixture_learnware_list = easy_market.search_learnware(user_info)

            print(f"search result of user{idx}:")
            for score, learnware in zip(sorted_score_list, single_learnware_list):
                print(f"score: {score}, learnware_id: {learnware.id}")
            mixture_id = " ".join([learnware.id for learnware in mixture_learnware_list])
            print(f"mixture_learnware: {mixture_id}\n")

        rmtree(test_folder)  # rm -r test_folder


if __name__ == "__main__":
    fire.Fire(LearnwareMarketWorkflow)