[MNT] refactor client load test

tests/test_learnware_client/test_load_conda.py

@@ -1,82 +0,0 @@
-import os
-import unittest
-import zipfile
-import numpy as np
-
-import learnware
-from learnware.learnware import get_learnware_from_dirpath
-from learnware.client import LearnwareClient
-from learnware.client.container import ModelCondaContainer, LearnwaresContainer
-from learnware.reuse import AveragingReuser
-
-
-class TestLearnwareLoad(unittest.TestCase):
-    def setUp(self):
-        unittest.TestCase.setUpClass()
-        self.client = LearnwareClient()
-
-        root = os.path.dirname(__file__)
-        self.learnware_ids = ["00000084", "00000154", "00000155"]
-        self.zip_paths = [os.path.join(root, x) for x in ["1.zip", "2.zip", "3.zip"]]
-
-    def test_load_single_learnware_by_zippath(self):
-        for learnware_id, zip_path in zip(self.learnware_ids, self.zip_paths):
-            self.client.download_learnware(learnware_id, zip_path)
-
-        learnware_list = [
-            self.client.load_learnware(learnware_path=zippath, runnable_option="conda") for zippath in self.zip_paths
-        ]
-        reuser = AveragingReuser(learnware_list, mode="vote_by_label")
-        input_array = np.random.random(size=(20, 13))
-        print(reuser.predict(input_array))
-
-        for learnware in learnware_list:
-            print(learnware.id, learnware.predict(input_array))
-
-    def test_load_multi_learnware_by_zippath(self):
-        for learnware_id, zip_path in zip(self.learnware_ids, self.zip_paths):
-            self.client.download_learnware(learnware_id, zip_path)
-
-        learnware_list = self.client.load_learnware(learnware_path=self.zip_paths, runnable_option="conda")
-        reuser = AveragingReuser(learnware_list, mode="vote_by_label")
-        input_array = np.random.random(size=(20, 13))
-        print(reuser.predict(input_array))
-
-        for learnware in learnware_list:
-            print(learnware.id, learnware.predict(input_array))
-
-    def test_load_single_learnware_by_id(self):
-        learnware_list = [
-            self.client.load_learnware(learnware_id=idx, runnable_option="conda") for idx in self.learnware_ids
-        ]
-        reuser = AveragingReuser(learnware_list, mode="vote_by_label")
-        input_array = np.random.random(size=(20, 13))
-        print(reuser.predict(input_array))
-
-        for learnware in learnware_list:
-            print(learnware.id, learnware.predict(input_array))
-
-    def test_load_multi_learnware_by_id(self):
-        learnware_list = self.client.load_learnware(learnware_id=self.learnware_ids, runnable_option="conda")
-        reuser = AveragingReuser(learnware_list, mode="vote_by_label")
-        input_array = np.random.random(size=(20, 13))
-        print(reuser.predict(input_array))
-
-        for learnware in learnware_list:
-            print(learnware.id, learnware.predict(input_array))
-
-    def test_load_single_learnware_by_id_pip(self):
-        learnware_id = "00000147"
-        learnware = self.client.load_learnware(learnware_id=learnware_id, runnable_option="conda")
-        input_array = np.random.random(size=(20, 23))
-        print(learnware.predict(input_array))
-
-    def test_load_single_learnware_by_id_conda(self):
-        learnware_id = "00000148"
-        learnware = self.client.load_learnware(learnware_id=learnware_id, runnable_option="conda")
-        input_array = np.random.random(size=(20, 204))
-        print(learnware.predict(input_array))
-
-
-if __name__ == "__main__":
-    unittest.main()

tests/test_learnware_client/test_load_docker.py

@@ -1,57 +0,0 @@
-import os
-import unittest
-import zipfile
-import numpy as np
-
-import learnware
-from learnware.learnware import get_learnware_from_dirpath
-from learnware.client import LearnwareClient
-from learnware.client.container import ModelCondaContainer, LearnwaresContainer
-from learnware.reuse import AveragingReuser
-
-
-class TestLearnwareLoad(unittest.TestCase):
-    def setUp(self):
-        unittest.TestCase.setUpClass()
-        self.client = LearnwareClient()
-
-        root = os.path.dirname(__file__)
-        self.learnware_ids = ["00000084", "00000154", "00000155"]
-        self.zip_paths = [os.path.join(root, x) for x in ["1.zip", "2.zip", "3.zip"]]
-
-    def test_load_multi_learnware_by_zippath(self):
-        for learnware_id, zip_path in zip(self.learnware_ids, self.zip_paths):
-            self.client.download_learnware(learnware_id, zip_path)
-
-        learnware_list = self.client.load_learnware(learnware_path=self.zip_paths, runnable_option="docker")
-        reuser = AveragingReuser(learnware_list, mode="vote_by_label")
-        input_array = np.random.random(size=(20, 13))
-        print(reuser.predict(input_array))
-
-        for learnware in learnware_list:
-            print(learnware.id, learnware.predict(input_array))
-
-    def test_load_multi_learnware_by_id(self):
-        learnware_list = self.client.load_learnware(learnware_id=self.learnware_ids, runnable_option="docker")
-        reuser = AveragingReuser(learnware_list, mode="vote_by_label")
-        input_array = np.random.random(size=(20, 13))
-        print(reuser.predict(input_array))
-
-        for learnware in learnware_list:
-            print(learnware.id, learnware.predict(input_array))
-
-    def test_load_single_learnware_by_id_pip(self):
-        learnware_id = "00000147"
-        learnware = self.client.load_learnware(learnware_id=learnware_id, runnable_option="docker")
-        input_array = np.random.random(size=(20, 23))
-        print(learnware.predict(input_array))
-
-    def test_load_single_learnware_by_id_conda(self):
-        learnware_id = "00000148"
-        learnware = self.client.load_learnware(learnware_id=learnware_id, runnable_option="docker")
-        input_array = np.random.random(size=(20, 204))
-        print(learnware.predict(input_array))
-
-
-if __name__ == "__main__":
-    unittest.main()

tests/test_learnware_client/test_load_learnware.py

@@ -0,0 +1,101 @@
+import os
+import unittest
+import argparse
+import numpy as np
+
+import learnware
+from learnware.learnware import get_learnware_from_dirpath
+from learnware.client import LearnwareClient
+from learnware.client.container import ModelCondaContainer, LearnwaresContainer
+from learnware.reuse import AveragingReuser
+
+
+class TestLearnwareLoadWithConda(unittest.TestCase):
+    def setUp(self):
+        self.client = LearnwareClient()
+        root = os.path.dirname(__file__)
+        self.learnware_ids = ["00000084", "00000154", "00000155"]
+        self.zip_paths = [os.path.join(root, x) for x in ["1.zip", "2.zip", "3.zip"]]
+        self.runnable_option = "conda"
+
+    #def test_load_single_learnware_by_zippath(self):
+    #    for learnware_id, zip_path in zip(self.learnware_ids, self.zip_paths):
+    #        self.client.download_learnware(learnware_id, zip_path)
+#
+    #    learnware_list = [
+    #        self.client.load_learnware(learnware_path=zippath, runnable_option=self.runnable_option) for zippath in self.zip_paths
+    #    ]
+    #    reuser = AveragingReuser(learnware_list, mode="vote_by_label")
+    #    input_array = np.random.random(size=(20, 13))
+    #    print(reuser.predict(input_array))
+#
+    #    for learnware in learnware_list:
+    #        print(learnware.id, learnware.predict(input_array))
+#
+    #def test_load_multi_learnware_by_zippath(self):
+    #    for learnware_id, zip_path in zip(self.learnware_ids, self.zip_paths):
+    #        self.client.download_learnware(learnware_id, zip_path)
+#
+    #    learnware_list = self.client.load_learnware(learnware_path=self.zip_paths, runnable_option=self.runnable_option)
+    #    reuser = AveragingReuser(learnware_list, mode="vote_by_label")
+    #    input_array = np.random.random(size=(20, 13))
+    #    print(reuser.predict(input_array))
+#
+    #    for learnware in learnware_list:
+    #        print(learnware.id, learnware.predict(input_array))
+#
+    #def test_load_single_learnware_by_id(self):
+    #    learnware_list = [
+    #        self.client.load_learnware(learnware_id=idx, runnable_option=self.runnable_option) for idx in self.learnware_ids
+    #    ]
+    #    reuser = AveragingReuser(learnware_list, mode="vote_by_label")
+    #    input_array = np.random.random(size=(20, 13))
+    #    print(reuser.predict(input_array))
+#
+    #    for learnware in learnware_list:
+    #        print(learnware.id, learnware.predict(input_array))
+#
+    #def test_load_multi_learnware_by_id(self):
+    #    learnware_list = self.client.load_learnware(learnware_id=self.learnware_ids, runnable_option=self.runnable_option)
+    #    reuser = AveragingReuser(learnware_list, mode="vote_by_label")
+    #    input_array = np.random.random(size=(20, 13))
+    #    print(reuser.predict(input_array))
+#
+    #    for learnware in learnware_list:
+    #        print(learnware.id, learnware.predict(input_array))
+#
+    def test_load_single_learnware_by_id_pip(self):
+        learnware_id = "00000147"
+        learnware = self.client.load_learnware(learnware_id=learnware_id, runnable_option=self.runnable_option)
+        input_array = np.random.random(size=(20, 23))
+        print(learnware.predict(input_array))
+#
+    def test_load_single_learnware_by_id_conda(self):
+        learnware_id = "00000148"
+        learnware = self.client.load_learnware(learnware_id=learnware_id, runnable_option=self.runnable_option)
+        input_array = np.random.random(size=(20, 204))
+        print(learnware.predict(input_array))
+#
+#
+class TestLearnwareLoadWithDocker(TestLearnwareLoadWithConda):
+    def setUp(self):
+        super(TestLearnwareLoadWithDocker, self).setUp()
+        self.runnable_option = "docker"
+
+def suite(mode):
+    _suite = unittest.TestSuite()
+    #_suite.addTest(TestLearnwareLoadWithDocker())
+    if mode == "all" or mode == "conda":
+        _suite.addTest(unittest.makeSuite(TestLearnwareLoadWithConda))
+    if mode == "all" or mode == "docker":
+        _suite.addTest(unittest.makeSuite(TestLearnwareLoadWithDocker))
+    return _suite
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--mode", type=str, required=False, default="all", help="The mode to run load learnware, must be in ['all', 'conda', 'docker']")
+    args = parser.parse_args()
+
+    assert args.mode in {"all", "conda", "docker"}, f"The mode must be in ['all', 'conda', 'docker'], instead of '{args.mode}'"
+    runner = unittest.TextTestRunner()
+    runner.run(suite(args.mode))

tests/test_learnware_client/test_reuse.py

@@ -1,34 +0,0 @@
-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))