learnware/tests/test_learnware_client/test_reuse.py

import zipfile
import numpy as np

from learnware.learnware import get_learnware_from_dirpath, Learnware
from learnware.market import EasyMarket
from learnware.client.container import ModelEnvContainer, LearnwaresContainer
from learnware.learnware.reuse import AveragingReuser

if __name__ == "__main__":
    semantic_specification = dict()
    semantic_specification["Data"] = {"Type": "Class", "Values": ["Text"]}
    semantic_specification["Task"] = {"Type": "Class", "Values": ["Ranking"]}
    semantic_specification["Library"] = {"Type": "Class", "Values": ["Scikit-learn"]}
    semantic_specification["Scenario"] = {"Type": "Tag", "Values": "Financial"}
    semantic_specification["Name"] = {"Type": "String", "Values": "test"}
    semantic_specification["Description"] = {"Type": "String", "Values": "test"}

    zip_paths = [
        "/home/bixd/workspace/learnware/Learnware/tests/test_learnware_client/rf_tic.zip",
        "/home/bixd/workspace/learnware/Learnware/tests/test_learnware_client/svc_tic.zip",
    ]
    dir_paths = [
        "/home/bixd/workspace/learnware/Learnware/tests/test_learnware_client/rf_tic",
        "/home/bixd/workspace/learnware/Learnware/tests/test_learnware_client/svc_tic",
    ]

    learnware_list = []
    for id, (zip_path, dir_path) in enumerate(zip(zip_paths, dir_paths)):
        with zipfile.ZipFile(zip_path, "r") as z_file:
            z_file.extractall(dir_path)

        learnware = get_learnware_from_dirpath(f"test_id{id}", semantic_specification, dir_path)
        learnware_list.append(learnware)

    with LearnwaresContainer(learnware_list, zip_paths) as env_container:
        learnware_list = env_container.get_learnware_list_with_container()
        reuser = AveragingReuser(learnware_list, mode="vote")
        input_array = np.random.randint(0, 3, size=(20, 9))
        print(reuser.predict(input_array).argmax(axis=1))
        for id, ind_learner in enumerate(learnware_list):
            print(f"learner_{id}", reuser.predict(input_array).argmax(axis=1))