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- import zipfile
- import numpy as np
-
- from learnware.learnware import get_learnware_from_dirpath
- from learnware.client.container import LearnwaresContainer
- from learnware.reuse import AveragingReuser
- from learnware.tests.module import get_semantic_specification
-
- if __name__ == "__main__":
- semantic_specification = get_semantic_specification()
- 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) as env_container:
- learnware_list = env_container.get_learnwares_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))
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