[ENH] add organizer, searcher, checker for market

learnware/market/base.py

@@ -1,11 +1,14 @@
 import os
+import torch
+import traceback
 import numpy as np
-import pandas as pd
-from typing import Tuple, Any, List, Union, Dict
 
+
+from typing import Tuple, Any, List, Union
 from ..learnware import Learnware
-from ..specification import RKMEStatSpecification
+from ..logger import get_module_logger
 
+logger = get_module_logger("market_base", "INFO")
 
 class BaseUserInfo:
     """User Information for searching learnware"""
@@ -43,19 +46,13 @@ class BaseUserInfo:
 class BaseMarket:
     """Base interface for market, it provide the interface of search/add/detele/update learnwares"""
 
-    def __init__(self, market_id: str = None):
+    def __init__(self, market_id: str = None, checker: 'LearnwareChecker' = None):
         self.market_id = market_id
+        
+        self.learnware_checker = LearnwareChecker() if checker is None else checker
 
-    def reload_market(self, market_path: str, semantic_spec_list_path: str) -> bool:
+    def reload_market(self, **kwargs) -> bool:
         """Reload the market when server restared.
-
-        Parameters
-        ----------
-        market_path : str
-            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.
-
         Returns
         -------
         bool
@@ -64,8 +61,7 @@ class BaseMarket:
 
         raise NotImplementedError("reload market is Not Implemented")
 
-    @classmethod
-    def check_learnware(cls, learnware: Learnware) -> bool:
+    def check_learnware(self, learnware: Learnware) -> bool:
         """Check the utility of a learnware
 
         Parameters
@@ -77,7 +73,7 @@ class BaseMarket:
         bool
             A flag indicating whether the learnware can be accepted.
         """
-        return True
+        return self.learnware_checker(learnware)
 
     def add_learnware(
         self, learnware_name: str, model_path: str, stat_spec_path: str, semantic_spec: dict, desc: str
@@ -221,9 +217,7 @@ class LearnwareOrganizer:
 
         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]:
+    def add_learnware(self, zip_path: str, semantic_spec: dict) -> Tuple[str, bool]:
         """Add a learnware into the market.
 
         .. note::
@@ -233,22 +227,17 @@ class LearnwareOrganizer:
 
         Parameters
         ----------
-        learnware_name : str
-            Name of new learnware.
-        model_path : str
+        zip_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.
+        Tuple[str, int]
+            - str indicating model_id
+            - int indicating what the flag of learnware is added.
+
 
         Raises
         ------
@@ -280,7 +269,33 @@ class LearnwareOrganizer:
         raise NotImplementedError("delete learnware is Not Implemented")
 
 class LearnwareSearcher:
-    def __init__(self, learnware_organizor):
+    def __init__(self, organizer):
+        self.learnware_organizer = organizer
+    
+    def __call__(self, user_info: BaseUserInfo):
+        raise NotImplementedError("'__call__' method is not implemented in LearnwareSearcher")
         
-    def search_learnware(self, user_info: BaseUserInfo) -> Tuple[Any, List[Learnware]]:
-        pass
+
+class LearnwareChecker:
+    INVALID_LEARNWARE = -1
+    NONUSABLE_LEARNWARE = 0
+    USABLE_LEARWARE = 1
+    
+    @classmethod
+    def __call__(cls, learnware: Learnware) -> int:
+        """Check the utility of a learnware
+
+        Parameters
+        ----------
+        learnware : Learnware
+
+        Returns
+        -------
+        int
+            A flag indicating whether the learnware can be accepted.
+            - The INVALID_LEARNWARE denotes the learnware does not pass the check
+            - The NOPREDICTION_LEARNWARE denotes the learnware pass the check but cannot make prediction due to some env dependency
+            - The NOPREDICTION_LEARNWARE denotes the leanrware pass the check and can make prediction
+        """
+        
+        raise NotImplementedError("'__call__' method is not implemented in LearnwareChecker")

learnware/market/easy/__init__.py

@@ -0,0 +1,7 @@
+from ..base import LearnwareSearcher, LearnwareOrganizer
+
+class EasySearcher(LearnwareSearcher):
+    pass
+
+class EasyOrganizer(LearnwareOrganizer):
+    pass

learnware/market/easy/checker.py

@@ -0,0 +1,72 @@
+import traceback
+
+from ..base import LearnwareChecker
+from ...logger import get_module_logger
+
+logger = get_module_logger("easy_checker", "INFO")
+
+class EasyChecker(LearnwareChecker):
+    
+    @classmethod
+    def __call__(cls, learnware):
+        semantic_spec = learnware.get_specification().get_semantic_spec()
+
+        try:
+            # check model instantiation
+            learnware.instantiate_model()
+
+        except Exception as e:
+            traceback.print_exc()
+            logger.warning(f"The learnware [{learnware.id}] is instantiated failed! Due to {e}")
+            return cls.NONUSABLE_LEARNWARE
+
+        try:
+            learnware_model = learnware.get_model()
+
+            # check input shape
+            if semantic_spec["Data"]["Values"][0] == "Table":
+                input_shape = (semantic_spec["Input"]["Dimension"],)
+            else:
+                input_shape = learnware_model.input_shape
+                pass
+
+            # check rkme dimension
+            stat_spec = learnware.get_specification().get_stat_spec_by_name("RKMEStatSpecification")
+            if stat_spec is not None:
+                if stat_spec.get_z().shape[1:] != input_shape:
+                    logger.warning(f"The learnware [{learnware.id}] input dimension mismatch with stat specification")
+                    return cls.NONUSABLE_LEARNWARE
+                pass
+
+            inputs = np.random.randn(10, *input_shape)
+            outputs = learnware.predict(inputs)
+
+            # check output
+            if outputs.ndim == 1:
+                outputs = outputs.reshape(-1, 1)
+                pass
+
+            if semantic_spec["Task"]["Values"][0] in ("Classification", "Regression", "Feature Extraction"):
+                # check output type
+                if isinstance(outputs, torch.Tensor):
+                    outputs = outputs.detach().cpu().numpy()
+                if not isinstance(outputs, np.ndarray):
+                    logger.warning(f"The learnware [{learnware.id}] output must be np.ndarray or torch.Tensor")
+                    return cls.NONUSABLE_LEARNWARE
+
+                # check output shape
+                output_dim = int(semantic_spec["Output"]["Dimension"])
+                if outputs[0].shape[0] != output_dim:
+                    logger.warning(f"The learnware [{learnware.id}] input and output dimention is error")
+                    return cls.NONUSABLE_LEARNWARE
+                pass
+            else:
+                if outputs.shape[1:] != learnware_model.output_shape:
+                    logger.warning(f"The learnware [{learnware.id}] input and output dimention is error")
+                    return cls.NONUSABLE_LEARNWARE
+
+        except Exception as e:
+            logger.warning(f"The learnware [{learnware.id}] prediction is not avaliable! Due to {repr(e)}")
+            return cls.NONUSABLE_LEARNWARE
+
+        return cls.USABLE_LEARWARE

learnware/market/easy/organizer.py

@@ -0,0 +1,11 @@
+import traceback
+
+from ..base import LearnwareOrganizer
+from ...logger import get_module_logger
+
+logger = get_module_logger("easy_organizer")
+
+
+class EasyOrganizer(LearnwareOrganizer):
+    
+    

learnware/market/easy/searcher.py

@@ -0,0 +1,8 @@
+from ..base import LearnwareSearcher
+from ...logger import get_module_logger
+
+logger = get_module_logger('easy_seacher')
+
+class EasySearcher(LearnwareSearcher):
+    pass
+    

learnware/market/searcher.py

@@ -0,0 +1,10 @@
+
+
+from typing import Tuple, Any, List
+
+from .base import BaseUserInfo
+from ..learnware import Learnware
+from ..logger import get_module_logger
+
+logger = get_module_logger('model')
+