diff --git a/examples/example_market_db/example_db.py b/examples/example_market_db/example_db.py index 5e63b40..9aaca9c 100644 --- a/examples/example_market_db/example_db.py +++ b/examples/example_market_db/example_db.py @@ -14,10 +14,7 @@ curr_root = os.path.dirname(os.path.abspath(__file__)) semantic_specs = [ { "Data": {"Values": ["Tabular"], "Type": "Class"}, - "Task": { - "Values": ["Classification"], - "Type": "Class", - }, + "Task": {"Values": ["Classification"], "Type": "Class"}, "Device": {"Values": ["GPU"], "Type": "Tag"}, "Scenario": {"Values": ["Nature"], "Type": "Tag"}, "Description": {"Values": "", "Type": "Description"}, @@ -25,10 +22,7 @@ semantic_specs = [ }, { "Data": {"Values": ["Tabular"], "Type": "Class"}, - "Task": { - "Values": ["Classification"], - "Type": "Class", - }, + "Task": {"Values": ["Classification"], "Type": "Class"}, "Device": {"Values": ["GPU"], "Type": "Tag"}, "Scenario": {"Values": ["Business", "Nature"], "Type": "Tag"}, "Description": {"Values": "", "Type": "Description"}, @@ -36,10 +30,7 @@ semantic_specs = [ }, { "Data": {"Values": ["Tabular"], "Type": "Class"}, - "Task": { - "Values": ["Classification"], - "Type": "Class", - }, + "Task": {"Values": ["Regression"], "Type": "Class"}, "Device": {"Values": ["GPU"], "Type": "Tag"}, "Scenario": {"Values": ["Business"], "Type": "Tag"}, "Description": {"Values": "", "Type": "Description"}, @@ -49,14 +40,11 @@ semantic_specs = [ user_senmantic = { "Data": {"Values": ["Tabular"], "Type": "Class"}, - "Task": { - "Values": ["Classification"], - "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"}, + "Name": {"Values": "learnware_4", "Type": "Name"}, } @@ -130,15 +118,20 @@ def test_search_semantics(): 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}") + 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, stat_info={"RKME": user_spec}) - sorted_dist_list, single_learnware_list, mixture_learnware_list = easy_market.search_learnware(user_info) + 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}") @@ -160,11 +153,11 @@ def test_stat_search(): user_info = BaseUserInfo( id="user_0", semantic_spec=user_senmantic, stat_info={"RKMEStatSpecification": user_spec} ) - sorted_dist_list, single_learnware_list, mixture_learnware_list = easy_market.search_learnware(user_info) + sorted_score_list, single_learnware_list, mixture_learnware_list = easy_market.search_learnware(user_info) print(f"search result of user{idx}:") - for dist, learnware in zip(sorted_dist_list, single_learnware_list): - print(f"dist: {dist}, learnware_id: {learnware.id}") + 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") @@ -175,5 +168,5 @@ if __name__ == "__main__": learnware_num = 5 prepare_learnware(learnware_num) test_market() - test_stat_search() + # test_stat_search() test_search_semantics() diff --git a/learnware/config.py b/learnware/config.py index 2e10988..da10c9b 100644 --- a/learnware/config.py +++ b/learnware/config.py @@ -66,10 +66,7 @@ os.makedirs(LEARNWARE_FOLDER_POOL_PATH, exist_ok=True) os.makedirs(DATABASE_PATH, exist_ok=True) semantic_config = { - "Data": { - "Values": ["Tabular", "Image", "Video", "Text", "Audio"], - "Type": "Class", # Choose only one class - }, + "Data": {"Values": ["Tabular", "Image", "Video", "Text", "Audio"], "Type": "Class",}, # Choose only one class "Task": { "Values": [ "Classification", @@ -82,10 +79,7 @@ semantic_config = { ], "Type": "Class", # Choose only one class }, - "Device": { - "Values": ["CPU", "GPU"], - "Type": "Tag", # Choose one or more tags - }, + "Device": {"Values": ["CPU", "GPU"], "Type": "Tag",}, # Choose one or more tags "Scenario": { "Values": [ "Business", @@ -105,14 +99,8 @@ semantic_config = { ], "Type": "Tag", # Choose one or more tags }, - "Description": { - "Values": None, - "Type": "Description", - }, - "Name": { - "Values": None, - "Type": "Name", - }, + "Description": {"Values": None, "Type": "Description",}, + "Name": {"Values": None, "Type": "Name",}, } _DEFAULT_CONFIG = { diff --git a/learnware/learnware/__init__.py b/learnware/learnware/__init__.py index aa24867..899abd9 100644 --- a/learnware/learnware/__init__.py +++ b/learnware/learnware/__init__.py @@ -29,10 +29,7 @@ def get_learnware_from_dirpath(id: str, semantic_spec: dict, learnware_dirpath: The contructed learnware object, return None if build failed """ learnware_config = { - "model": { - "class_name": "Model", - "kwargs": {}, - }, + "model": {"class_name": "Model", "kwargs": {},}, "stat_specifications": [ { "module_path": "learnware.specification", diff --git a/learnware/market/easy.py b/learnware/market/easy.py index 8b7aeaf..2e889e6 100644 --- a/learnware/market/easy.py +++ b/learnware/market/easy.py @@ -119,12 +119,27 @@ class EasyMarket(BaseMarket): self.learnware_folder_list[id] = target_folder_dir self.count += 1 add_learnware_to_db( - id, - semantic_spec=semantic_spec, - zip_path=target_folder_dir, - folder_path=target_folder_dir, + id, semantic_spec=semantic_spec, zip_path=target_folder_dir, folder_path=target_folder_dir, ) return id, True + + def _convert_dist_to_score(self, dist_list: List[float]) -> List[float]: + """Convert mmd dist list into min_max score list + + Parameters + ---------- + dist_list : List[float] + The list of mmd distances from learnware rkmes to user rkme + + Returns + ------- + List[float] + The list of min_max scores of each learnware + """ + if max(dist_list) == min(dist_list): + return [1 for dist in dist_list] + else: + return [(max(dist_list) - dist) / (max(dist_list) - min(dist_list)) for dist in dist_list] def _calculate_rkme_spec_mixture_weight( self, @@ -317,21 +332,6 @@ class EasyMarket(BaseMarket): return sorted_dist_list, sorted_learnware_list - def _search_by_semantic_description( - self, learnware_list: List[Learnware], user_info: BaseUserInfo - ) -> List[Learnware]: - user_semantic_spec = user_info.get_semantic_spec() - user_input_description = user_semantic_spec["Description"]["Values"] - if not user_input_description: - return [] - match_learnwares = [] - for learnware in learnware_list: - learnware_semantic_spec = learnware.get_specification().get_semantic_spec() - learnware_name = learnware_semantic_spec["Name"]["Values"] - if user_input_description in learnware_name: - match_learnwares.append(learnware) - return match_learnwares - def _search_by_semantic_tags(self, learnware_list: List[Learnware], user_info: BaseUserInfo) -> List[Learnware]: def match_semantic_tags(semantic_spec1, semantic_spec2): if semantic_spec1.keys() != semantic_spec2.keys(): @@ -339,12 +339,23 @@ class EasyMarket(BaseMarket): logger.warning("semantic_spec key error!") return False for key in semantic_spec1.keys(): + if len(semantic_spec1[key]["Values"]) == 0: + continue + if len(semantic_spec2[key]["Values"]) == 0: + continue if semantic_spec1[key]["Type"] == "Class": + if isinstance(semantic_spec1[key]["Values"], list): + semantic_spec1[key]["Values"] = semantic_spec1[key]["Values"][0] + if isinstance(semantic_spec2[key]["Values"], list): + semantic_spec2[key]["Values"] = semantic_spec2[key]["Values"][0] if semantic_spec1[key]["Values"] != semantic_spec2[key]["Values"]: return False elif semantic_spec1[key]["Type"] == "Tag": if not (set(semantic_spec1[key]["Values"]) & set(semantic_spec2[key]["Values"])): return False + elif semantic_spec1[key]["Type"] == "Name": + if semantic_spec2[key]["Values"] not in semantic_spec1[key]["Values"]: + return False return True match_learnwares = [] @@ -375,19 +386,19 @@ class EasyMarket(BaseMarket): the third is the list of Learnware (mixture), the size is search_num """ learnware_list = [self.learnware_list[key] for key in self.learnware_list] - learnware_list_tags = self._search_by_semantic_tags(learnware_list, user_info) - learnware_list_description = self._search_by_semantic_description(learnware_list, user_info) - learnware_list = list(set(learnware_list_tags + learnware_list_description)) + learnware_list = self._search_by_semantic_tags(learnware_list, user_info) + # learnware_list = list(set(learnware_list_tags + learnware_list_description)) if "RKMEStatSpecification" not in user_info.stat_info: return None, learnware_list, None else: user_rkme = user_info.stat_info["RKMEStatSpecification"] sorted_dist_list, single_learnware_list = self._search_by_rkme_spec_single(learnware_list, user_rkme) + sorted_score_list = self._convert_dist_to_score(sorted_dist_list) weight_list, mixture_learnware_list = self._search_by_rkme_spec_mixture( learnware_list, user_rkme, search_num ) - return sorted_dist_list, single_learnware_list, mixture_learnware_list + return sorted_score_list, single_learnware_list, mixture_learnware_list def delete_learnware(self, id: str) -> bool: """Delete Learnware from market diff --git a/learnware/specification/base.py b/learnware/specification/base.py index d9e208a..449cfd3 100644 --- a/learnware/specification/base.py +++ b/learnware/specification/base.py @@ -7,10 +7,10 @@ class BaseStatSpecification: pass def generate_stat_spec_from_data(self, **kwargs): - """Construct reduced set from raw dataset using iterative optimization + """Construct statistical specification from raw dataset - kwargs may include the feature, label and model - - kwargs also can include hyperparameter for specifaction generation + - kwargs also can include hyperparameters of specific method for specifaction generation """ raise NotImplementedError("generate_stat_spec_from_data is not implemented") diff --git a/learnware/specification/rkme.py b/learnware/specification/rkme.py index 9668396..568c410 100644 --- a/learnware/specification/rkme.py +++ b/learnware/specification/rkme.py @@ -255,9 +255,7 @@ class RKMEStatSpecification(BaseStatSpecification): rkme_to_save["beta"] = rkme_to_save["beta"].tolist() rkme_to_save["device"] = "gpu" if rkme_to_save["cuda_idx"] != -1 else "cpu" json.dump( - rkme_to_save, - codecs.open(save_path, "w", encoding="utf-8"), - separators=(",", ":"), + rkme_to_save, codecs.open(save_path, "w", encoding="utf-8"), separators=(",", ":"), ) def load(self, filepath: str) -> bool: @@ -345,7 +343,7 @@ def torch_rbf_kernel(x1, x2, gamma) -> torch.Tensor: """ x1 = x1.double() x2 = x2.double() - X12norm = torch.sum(x1**2, 1, keepdim=True) - 2 * x1 @ x2.T + torch.sum(x2**2, 1, keepdim=True).T + X12norm = torch.sum(x1 ** 2, 1, keepdim=True) - 2 * x1 @ x2.T + torch.sum(x2 ** 2, 1, keepdim=True).T return torch.exp(-X12norm * gamma)