[ENH] Restruct Market Class

docs/references/api.rst

@@ -17,10 +17,10 @@ Market
 .. autoclass:: learnware.market.AnchoredMarket
     :members:
 
-.. autoclass:: learnware.market.BaseMarket
+.. autoclass:: learnware.market.EvolvedMarket
     :members:
 
-.. autoclass:: learnware.market.BaseReuse
+.. autoclass:: learnware.market.BaseUserInfo
     :members:
 
 Learnware
@@ -29,6 +29,8 @@ Learnware
 .. autoclass:: learnware.learnware.Learnware
     :members:
 
+.. autoclass:: learnware.learnware.BaseReuse
+    :members:
 
 Specification
 ====================
@@ -36,7 +38,7 @@ Specification
 .. autoclass:: learnware.specification.Specification
     :members:
 
-.. autoclass:: learnware.specification.StatSpecification
+.. autoclass:: learnware.specification.BaseStatSpecification
     :members:
 
 .. autoclass:: learnware.specification.RKMESpecification

learnware/market/base.py

@@ -48,60 +48,7 @@ class BaseMarket:
         learnmarket = BaseMarket()
     """
 
-    def __init__(self):
-        """Initializing an empty market"""
-        self.learnware_list = {}  # id: Learnware
-        self.count = 0
-        self.semantic_spec_list = self._init_semantic_spec_list()
-
-    def _init_semantic_spec_list(self):
-        return {
-            "Data": {
-                "Values": ["Tabular", "Image", "Video", "Text", "Audio"],
-                "Type": "Class",  # Choose only one class
-            },
-            "Task": {
-                "Values": [
-                    "Classification",
-                    "Regression",
-                    "Clustering",
-                    "Feature Extraction",
-                    "Generation",
-                    "Segmentation",
-                    "Object Detection",
-                ],
-                "Type": "Class",  # Choose only one class
-            },
-            "Device": {
-                "Values": ["CPU", "GPU"],
-                "Type": "Tag",  # Choose one or more tags
-            },
-            "Scenario": {
-                "Values": [
-                    "Business",
-                    "Financial",
-                    "Health",
-                    "Politics",
-                    "Computer",
-                    "Internet",
-                    "Traffic",
-                    "Nature",
-                    "Fashion",
-                    "Industry",
-                    "Agriculture",
-                    "Education",
-                    "Entertainment",
-                    "Architecture",
-                ],
-                "Type": "Tag",  # Choose one or more tags
-            },
-            "Description": {
-                "Values": str,
-                "Type": "Description",
-            },
-        }
-
-    def reload_market(self, market_path: str, semantic_spec_list_path: str, load_mode: str = "database") -> bool:
+    def reload_market(self, market_path: str, semantic_spec_list_path: str) -> bool:
         """Reload the market when server restared.
 
         Parameters
@@ -110,32 +57,14 @@ class BaseMarket:
             Directory for market data. '_IP_:_port_' for loading from database.
         semantic_spec_list_path : str
             Directory for available semantic_spec. Should be a json file.
-        load_mode : str, optional
-            Type of reload source. Currently, only 'database' is available. Defaults to 'database', by default "database"
 
         Returns
         -------
         bool
             A flag indicating whether the market is reload successfully.
-
-        Raises
-        ------
-        NotImplementedError
-            Reload method NOT implemented. Currently, only loading from database is supported.
-        FileNotFoundError
-            Loading source/semantic_spec_list NOT found. Check whether the source and semantic_spec_list are available.
-
         """
 
-        if load_mode == "database":
-            pass
-        else:
-            # May Support other loading methods in the future
-            raise NotImplementedError("reload_market from {} is NOT implemented".format(load_mode))
-
-        raise FileNotFoundError("Reload source NOT Found!")
-
-        return True
+        raise NotImplementedError("reload market is Not Implemented")
 
     def check_learnware(self, learnware: Learnware) -> bool:
         """Check the utility of a learnware
@@ -186,9 +115,7 @@ class BaseMarket:
             file for model or statistical specification not found
 
         """
-        if (not os.path.exists(model_path)) or (not os.path.exists(stat_spec_path)):
-            raise FileNotFoundError("Model or Stat_spec NOT Found.")
-        return str(self.count), True
+        raise NotImplementedError("add learnware is Not Implemented")
 
     def search_learnware(self, user_info: BaseUserInfo) -> Tuple[Any, List[Learnware]]:
         """Search Learnware based on user_info
@@ -207,30 +134,7 @@ class BaseMarket:
             - second is a list of matched learnwares
         """
 
-        def search_by_semantic_spec():
-            def match_semantic_spec(semantic_spec1, semantic_spec2):
-                if semantic_spec1.keys() != semantic_spec2.keys():
-                    raise Exception("semantic_spec key error".format(semantic_spec1.keys(), semantic_spec2.keys()))
-                for key in semantic_spec1.keys():
-                    if semantic_spec1[key]["Type"] == "Class":
-                        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
-                return True
-
-            match_learnwares = []
-            for learnware in self.learnware_list:
-                learnware_semantic_spec = learnware.get_specification().get_semantic_spec()
-                user_semantic_spec = user_info.get_semantic_spec()
-                if match_semantic_spec(learnware_semantic_spec, user_semantic_spec):
-                    match_learnwares.append(learnware)
-            return match_learnwares
-
-        match_learnwares = search_by_semantic_spec()
-
-        pass
+        raise NotImplementedError("search learnware is Not Implemented")
 
     def get_learnware_by_ids(self, id: Union[str, List[str]]) -> Union[Learnware, List[Learnware]]:
         """
@@ -249,7 +153,7 @@ class BaseMarket:
             - The returned items are search results.
             - 'None' indicating the target id not found.
         """
-        return None
+        raise NotImplementedError("search learnware is Not Implemented")
 
     def delete_learnware(self, id: str) -> bool:
         """Delete a learnware from market
@@ -269,11 +173,7 @@ class BaseMarket:
         Exception
             Raise an excpetion when given id is NOT found in learnware list
         """
-        if not id in self.learnware_list:
-            raise Exception("Learnware id:{} NOT Found!".format(id))
-
-        self.learnware_list.pop(id)
-        return True
+        raise NotImplementedError("delete learnware is Not Implemented")
 
     def update_learnware(self, id: str) -> bool:
         """
@@ -285,7 +185,7 @@ class BaseMarket:
         id : str
             id of target learnware.
         """
-        return True
+        raise NotImplementedError("update learnware is Not Implemented")
 
     def get_semantic_spec_list(self) -> dict:
         """Return all semantic specifications available
@@ -296,4 +196,4 @@ class BaseMarket:
             All emantic specifications in dictionary format
 
         """
-        return self.semantic_spec_list
+        raise NotImplementedError("get semantic spec list is not implemented")

learnware/market/serial.py

@@ -0,0 +1,138 @@
+import os
+import numpy as np
+import pandas as pd
+from typing import Tuple, Any, List, Union, Dict
+
+from .base import BaseMarket, BaseUserInfo
+from ..learnware import Learnware
+
+
+class SerialMarket(BaseMarket):
+    def __init__(self):
+        """Initializing an empty market"""
+        self.learnware_list = {}  # id: Learnware
+        self.count = 0
+        self.semantic_spec_list = self._init_semantic_spec_list()
+
+    def _init_semantic_spec_list(self):
+        return {
+            "Data": {
+                "Values": ["Tabular", "Image", "Video", "Text", "Audio"],
+                "Type": "Class",  # Choose only one class
+            },
+            "Task": {
+                "Values": [
+                    "Classification",
+                    "Regression",
+                    "Clustering",
+                    "Feature Extraction",
+                    "Generation",
+                    "Segmentation",
+                    "Object Detection",
+                ],
+                "Type": "Class",  # Choose only one class
+            },
+            "Device": {
+                "Values": ["CPU", "GPU"],
+                "Type": "Tag",  # Choose one or more tags
+            },
+            "Scenario": {
+                "Values": [
+                    "Business",
+                    "Financial",
+                    "Health",
+                    "Politics",
+                    "Computer",
+                    "Internet",
+                    "Traffic",
+                    "Nature",
+                    "Fashion",
+                    "Industry",
+                    "Agriculture",
+                    "Education",
+                    "Entertainment",
+                    "Architecture",
+                ],
+                "Type": "Tag",  # Choose one or more tags
+            },
+            "Description": {
+                "Values": str,
+                "Type": "Description",
+            },
+        }
+
+    def reload_market(self, market_path: str, semantic_spec_list_path: str) -> bool:
+        raise NotImplementedError("reload market is Not Implemented")
+
+    def add_learnware(
+        self, learnware_name: str, model_path: str, stat_spec_path: str, semantic_spec: dict, desc: str
+    ) -> Tuple[str, bool]:
+        """Add a learnware into the market.
+
+        .. note::
+
+            Given a prediction of a certain time, all signals before this time will be prepared well.
+
+
+        Parameters
+        ----------
+        learnware_name : str
+            Name of new learnware.
+        model_path : str
+            Filepath for learnware model, a zipped file.
+        stat_spec_path : str
+            Filepath for statistical specification, a '.npy' file.
+            How to pass parameters requires further discussion.
+        semantic_spec : dict
+            semantic_spec for new learnware, in dictionary format.
+        desc : str
+            Brief desciption for new learnware.
+
+        Returns
+        -------
+        Tuple[str, bool]
+            str indicating model_id, bool indicating whether the learnware is added successfully.
+
+        Raises
+        ------
+        FileNotFoundError
+            file for model or statistical specification not found
+
+        """
+        if (not os.path.exists(model_path)) or (not os.path.exists(stat_spec_path)):
+            raise FileNotFoundError("Model or Stat_spec NOT Found.")
+        return str(self.count), True
+
+    def search_learnware(self, user_info: BaseUserInfo) -> Tuple[Any, List[Learnware]]:
+        def search_by_semantic_spec():
+            def match_semantic_spec(semantic_spec1, semantic_spec2):
+                if semantic_spec1.keys() != semantic_spec2.keys():
+                    raise Exception("semantic_spec key error".format(semantic_spec1.keys(), semantic_spec2.keys()))
+                for key in semantic_spec1.keys():
+                    if semantic_spec1[key]["Type"] == "Class":
+                        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
+                return True
+
+            match_learnwares = []
+            for learnware in self.learnware_list:
+                learnware_semantic_spec = learnware.get_specification().get_semantic_spec()
+                user_semantic_spec = user_info.get_semantic_spec()
+                if match_semantic_spec(learnware_semantic_spec, user_semantic_spec):
+                    match_learnwares.append(learnware)
+            return match_learnwares
+
+        match_learnwares = search_by_semantic_spec()
+
+    def delete_learnware(self, id: str) -> bool:
+        if not id in self.learnware_list:
+            raise Exception("Learnware id:{} NOT Found!".format(id))
+
+        self.learnware_list.pop(id)
+        return True
+
+    def get_semantic_spec_list(self) -> dict:
+        return self.semantic_spec_list

learnware/specification/rkme.py

@@ -1,7 +1,8 @@
 from __future__ import annotations
 
 import os
-import mkl
+
+# import mkl
 import copy
 import torch
 import faiss
@@ -14,7 +15,7 @@ from typing import Tuple, Any, List, Union, Dict
 
 from .base import BaseStatSpecification
 
-mkl.get_max_threads()
+# mkl.get_max_threads()
 
 
 def setup_seed(seed):