learnware/examples/example_market_db/example_db.py

import os
import joblib
import numpy as np
from sklearn import svm

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"},
        "Library": {"Values": ["Scikit-learn"], "Type": "Class"},
        "Scenario": {"Values": ["Nature"], "Type": "Tag"},
        "Description": {"Values": "", "Type": "String"},
        "Name": {"Values": "learnware_1", "Type": "String"},
    },
    {
        "Data": {"Values": ["Tabular"], "Type": "Class"},
        "Task": {"Values": ["Classification"], "Type": "Class"},
        "Library": {"Values": ["Scikit-learn"], "Type": "Class"},
        "Scenario": {"Values": ["Business", "Nature"], "Type": "Tag"},
        "Description": {"Values": "", "Type": "String"},
        "Name": {"Values": "learnware_2", "Type": "String"},
    },
    {
        "Data": {"Values": ["Tabular"], "Type": "Class"},
        "Task": {"Values": ["Regression"], "Type": "Class"},
        "Library": {"Values": ["Scikit-learn"], "Type": "Class"},
        "Scenario": {"Values": ["Business"], "Type": "Tag"},
        "Description": {"Values": "", "Type": "String"},
        "Name": {"Values": "learnware_3", "Type": "String"},
    },
]

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


def prepare_learnware(learnware_num=10):
    np.random.seed(2023)
    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")
        os.system(f"cp example_init.py {init_file}")

        yaml_file = os.path.join(dir_path, "learnware.yaml")
        os.system(f"cp example.yaml {yaml_file}")

        zip_file = dir_path + ".zip"
        os.system(f"zip -q -r -j {zip_file} {dir_path}")
        os.system(f"rm -r {dir_path}")


def get_zip_path_list():
    root_path = os.path.join(curr_root, "learnware_pool")
    zip_path_list = [os.path.join(root_path, path) for path in os.listdir(root_path)]
    return zip_path_list


def test_market():
    database_ops.clear_learnware_table()
    easy_market = EasyMarket()
    print("Total Item:", len(easy_market))

    zip_path_list = get_zip_path_list()  # the path list for learnware .zip

    for idx, zip_path in enumerate(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:", curr_inds)

    easy_market.delete_learnware(curr_inds[3])
    easy_market.delete_learnware(curr_inds[2])
    curr_inds = easy_market._get_ids()
    print("Available ids:", curr_inds)


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

    root_path = "./learnware_pool"
    os.makedirs(root_path, exist_ok=True)
    test_learnware_num = 3
    prepare_learnware(test_learnware_num)

    test_folder = "./test_stat"
    zip_path_list = get_zip_path_list()

    idx, zip_path = 1, zip_path_list[1]
    unzip_dir = os.path.join(test_folder, f"{idx}")
    os.makedirs(unzip_dir, exist_ok=True)
    os.system(f"unzip -o -q {zip_path} -d {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)
    _, 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())

    os.system(f"rm -r {test_folder}")


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

    test_folder = "./test_stat"
    zip_path_list = get_zip_path_list()

    for idx, zip_path in enumerate(zip_path_list):
        unzip_dir = os.path.join(test_folder, f"{idx}")
        os.makedirs(unzip_dir, exist_ok=True)
        os.system(f"unzip -o -q {zip_path} -d {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")

    os.system(f"rm -r {test_folder}")


if __name__ == "__main__":
    learnware_num = 10
    prepare_learnware(learnware_num)
    test_market()
    test_stat_search()
    test_search_semantics()