[MNT] Modify details

examples/examples1/example_rkme.py

@@ -10,17 +10,17 @@
  spec2 = specification.rkme.RKMESpecification()
  spec1.generate_stat_spec_from_data(data_X)
  spec1.save("spec.json")
- 
+
  beta = spec1.get_beta()
  z = spec1.get_z()
  print(type(beta), beta.shape)
  print(type(z), z.shape)
- 
+
  spec2.load("spec.json")
  beta = spec1.get_beta()
  z = spec1.get_z()
  print(type(beta), beta.shape)
  print(type(z), z.shape)
- 
+
  print(spec1.inner_prod(spec2))
- print(spec1.dist(spec2))
+ print(spec1.dist(spec2))

examples/examples2/example_learnware.py

@@ -16,35 +16,26 @@ def prepare_learnware():
  clf = svm.SVC()
  clf.fit(data_X, data_y)
  joblib.dump(clf, "./svm/svm.pkl")
- 
- spec= specification.utils.generate_rkme_spec(X=data_X, gamma=0.1, cuda_idx=0)
+
+ spec = specification.utils.generate_rkme_spec(X=data_X, gamma=0.1, cuda_idx=0)
  spec.save("./svm/spec.json")
 
 
 def test_API():
- text_X = np.random.randn(100, 20) 
+ text_X = np.random.randn(100, 20)
  svm = SVM()
  pred_y1 = svm.predict(text_X)
  print(type(svm))
-
- model = {
- "module_path": "./svm/__init__.py",
- "class_name": "SVM"
- }
+
+ model = {"module_path": "./svm/__init__.py", "class_name": "SVM"}
  spec = specification.rkme.RKMESpecification()
  spec.load("./svm/spec.json")
- learnware = Learnware(
- id="A0",
- name="SVM",
- model=model,
- specification=spec,
- desc="svm"
- )
+ learnware = Learnware(id="A0", name="SVM", model=model, specification=spec, desc="svm")
  pred_y2 = learnware.predict(text_X)
  print(type(learnware.model))
  print(f"diff: {np.sum(pred_y1 != pred_y2)}")
 
 
 if __name__ == "__main__":
  prepare_learnware()
- test_API()
+ test_API()

examples/examples2/svm/__init__.py

@@ -6,7 +6,7 @@
 class SVM:
  def __init__(self):
  dir_path = os.path.dirname(os.path.abspath(__file__))
- self.model = joblib.load(os.path.join(dir_path, 'svm.pkl'))
+ self.model = joblib.load(os.path.join(dir_path, "svm.pkl"))
 
  def fit(self, X: np.ndarray, y: np.ndarray):
  pass
@@ -15,4 +15,4 @@ def predict(self, X: np.ndarray) -> np.ndarray:
  return self.model.predict(X)
 
  def fintune(self, X: np.ndarray, y: np.ndarray):
- pass
+ pass

learnware/learnware/base.py

@@ -48,7 +48,7 @@ def _import_model(self, model: Union[BaseModel, dict]) -> BaseModel:
 
  def predict(self, X: np.ndarray) -> np.ndarray:
  return self.model.predict(X)
- 
+
  def get_model(self) -> BaseModel:
  return self.model
 
@@ -57,10 +57,10 @@ def get_specification(self) -> Specification:
 
  def get_info(self):
  return self.desc
- 
+
  def update_stat_spec(self, name, new_stat_spec: BaseStatSpecification):
  self.specification.update_stat_spec(name, new_stat_spec)
- 
+
  def update(self):
  # Empty Interface.
  raise NotImplementedError("'update' Method is NOT Implemented.")

learnware/market/__init__.py

@@ -1,3 +1,3 @@
 from .base import BaseUserInfo, BaseMarket
 from .anchor import AnchoredUserInfo, AnchoredMarket
-from .evolve import EvolvedMarket
+from .evolve import EvolvedMarket

learnware/market/anchor.py

@@ -7,14 +7,14 @@
 
 class AnchoredUserInfo(BaseUserInfo):
  """
-  User Information for searching learnware (add the anchor design)
- 
-  - UserInfo contains the anchor list acquired from the market
-  - UserInfo can update stat_info based on anchors
+ User Information for searching learnware (add the anchor design)
+
+ - UserInfo contains the anchor list acquired from the market
+ - UserInfo can update stat_info based on anchors
  """
 
- def __init__(self, id: str, property: dict = dict(), stat_info: dict = dict()):
- super(AnchoredUserInfo, self).__init__(id, property, stat_info)
+ def __init__(self, id: str, semantic_spec: dict = dict(), stat_info: dict = dict()):
+ super(AnchoredUserInfo, self).__init__(id, semantic_spec, stat_info)
  self.anchor_learnware_list = {} # id: Learnware
 
  def add_anchor_learnware(self, learnware_id: str, learnware: Learnware):
@@ -79,7 +79,7 @@ def _delete_anchor_learnware(self, anchor_id: str) -> bool:
  -------
  bool
  True if the target anchor learnware is deleted successfully.
- 
+
  Raises
  ------
  Exception
@@ -107,7 +107,7 @@ def search_anchor_learnware(self, user_info: AnchoredUserInfo) -> Tuple[Any, Lis
  Parameters
  ----------
  user_info : AnchoredUserInfo
- - user_info with properties and statistical information
+ - user_info with semantic specifications and statistical information
  - some statistical information calculated on previous anchor learnwares
 
  Returns
@@ -126,7 +126,7 @@ def search_learnware(self, user_info: AnchoredUserInfo) -> Tuple[Any, List[Learn
  Parameters
  ----------
  user_info : AnchoredUserInfo
- - user_info with properties and statistical information
+ - user_info with semantic specifications and statistical information
  - some statistical information calculated on anchor learnwares
 
  Returns