[DOC] modify components/market doc

docs/components/market.rst

@@ -3,25 +3,52 @@
 Market
 ================================
 
-The learnware market receives high-performance machine learning models from developers, incorporates them into the system, and provides services to users by identifying and reusing learnware to help users solve current tasks. Developers voluntarily submit various learnwares to the learnware market, and the market conducts quality checks and further organization of these learnwares. When users submit task requirements, the learnware market automatically selects whether to recommend a single learnware or a combination of multiple learnwares. 
+The ``learnware market`` receives high-performance machine learning models from developers, incorporates them into the system, and provides services to users by identifying and reusing learnware to help users solve current tasks. Developers voluntarily submit various learnwares to the learnware market, and the market conducts quality checks and further organization of these learnwares. When users submit task requirements, the learnware market automatically selects whether to recommend a single learnware or a combination of multiple learnwares. 
 
-The learnware market will receive various kinds of learnwares, and learnwares from different feature/label spaces form numerous islands of specifications. All these islands together constitute the "specification world" in the learnware market. The market should discover and establish connections between different islands, and then merge them into a unified specification world. This further organization of learnwares can make the learnware search among all learnwares, not just learnwares which has the same feature space and label space with the user's task requirements.
+The ``learnware market`` will receive various kinds of learnwares, and learnwares from different feature/label spaces form numerous islands of specifications. All these islands together constitute the ``specification world`` in the learnware market. The market should discover and establish connections between different islands, and then merge them into a unified specification world. This further organization of learnwares support search learnwares among all learnwares, not just among learnwares which has the same feature space and label space with the user's task requirements.
 
 Framework
 ======================================
 
-The market class is initialized with a organizer class, a searcher class, and a list of checker classes. 
+The ``learnware market`` is combined with a ``organizer``, a ``searcher``, and a list of ``checker``s. 
 
-The organizer class should be able to organize the learnware in the market. It should be able to add, delete, and update learnware. It should also be able to search for learnware based on user requirement.
+The ``organizer`` can store and organize learnwares in the market. It supports ``add``, ``delete``, and ``update`` operations for learnwares. It also provides the interface for ``searcher`` to search learnwares based on user requirement.
 
-The searcher class should be able to search for learnware based on user requirement. It should be able to search for learnware based on user requirement.
+The ``searcher`` can search learnwares based on user requirement. The implementation of ``searcher`` is dependent on the concrete implementation and interface for ``organizer``, where usually an ``organizer`` can be compatible with multiple different ``searcher``s.
 
-The checker class is used for checking the learnware in some standards. It should check the utility of a learnware and is supposed to return the status and a message related to the learnware's check result.
+The ``checker`` is used for checking the learnware in some standards. It should check the utility of a learnware and is supposed to return the status and a message related to the learnware's check result. Only the learnwares who passed the ``checker`` could be able to be stored and added into the ``learnware market``. 
+
+
+
+Current Checkers
+======================================
+
+The ``learnware`` package provide two different implementation of ``market`` where both of them share the same ``checker`` list. So we first introduce the details of ``checker``s.
+
+The ``checker``s check a learnware object in different aspects, including environment configuration (``CondaChecker``), semantic specifications (``EasySemanticChecker``), and statistical specifications (``EasyStatChecker``). The ``__call__`` method of each checker is designed to be invoked as a function to conduct the respective checks on the learnware and return the outcomes. It defines three types of learnwares: ``INVALID_LEARNWARE`` denotes the learnware does not pass the check, ``NONUSABLE_LEARNWARE`` denotes the learnware pass the check but cannot make prediction, ``USABLE_LEARWARE`` denotes the leanrware pass the check and can make prediction. Currently, we have three ``checker``s, which are described below.
+
+
+``CondaChecker``
+------------------
+This ``checker`` checks a the environment of the learnware object. It creates a ``LearnwaresContainer`` instance to handle the Learnware and uses ``inner_checker`` to check the Learnware. If an exception occurs, it logs the error and returns ``NONUSABLE_LEARNWARE`` status and error message.
+
+
+``EasySemanticChecker``
+-------------------------
+This ``checker`` checks the semantic specification of a learnware object. It checks if the given semantic specification conforms to predefined standards. It verifies each key in predefined dictionary. If the check fails, it logs the error and returns ``NONUSABLE_LEARNWARE`` status and error message.
+
+
+``EasyStatChecker``
+---------------------
+
+This ``checker`` checks the statistical specification and functionality of a learnware object. It performs multiple checks to validate the learnware. It checks for model instantiation, verifies input shape and statistical specifications, and test output shape using random generated data. In case of any exceptions, it logs the error and returns ``NONUSABLE_LEARNWARE`` status and error message.
 
 
 Current Markets
 ======================================
 
+The ``learnware`` package provide two different implementation of ``market``, i.e. ``Easy Market`` and ``Hetero Market``. They have different implementation of ``organizer`` and ``searcher``.
+
 Easy Market
 -------------
 
@@ -37,20 +64,3 @@ One important case is that models have different feature spaces. In order to ena
 - First, design a method for the market to connect different feature spaces to a common subspace and implement the function ``HeterogeneousFeatureMarket.learn_mapping_functions``. This function uses specifications of all submitted models to learn mapping functions that can map the data in the original feature space to the common subspace and vice verse.
 - Second, use learned mapping functions to implement the functions ``HeterogeneousFeatureMarket.transform_original_to_subspace`` and ``HeterogeneousFeatureMarket.transform_subspace_to_original``.
 - Third, use the functions ``HeterogeneousFeatureMarket.transform_original_to_subspace`` and ``HeterogeneousFeatureMarket.transform_subspace_to_original`` to overwrite the mehtod ``EvolvedMarket.generate_new_stat_specification`` and  ``EvolvedMarket.EvolvedMarket.evolve_learnware_list`` of the base class ``EvolvedMarket``.
-
-Current Checkers
-======================================
-The checkers check a learnware object in different aspects, including environment configuration (``CondaChecker``), semantic specifications (``EasySemanticChecker``), and statistical specifications (``EasyStatChecker``). The ``__call__`` method of each checker is designed to be invoked as a function to conduct the respective checks on the learnware and return the outcomes. It defines three types of learnwares: INVALID_LEARNWARE denotes the learnware does not pass the check, NONUSABLE_LEARNWARE denotes the learnware pass the check but cannot make prediction due to some env dependency, USABLE_LEARWARE denotes the leanrware pass the check and can make prediction. Currently, we have three checkers, which are described below.
-
-
-CondaChecker Class
-------------------
-This checker checks a the environment of the learnware object. It creates a ``LearnwaresContainer`` instance to handle the Learnware and uses ``inner_checker`` to check the Learnware. If an exception occurs, it logs the error and returns ``BaseChecker.NONUSABLE_LEARNWARE`` status and error message.
-
-EasySemanticChecker Class
--------------------------
-This checker checks the semantic specification of a learnware object. It checks if the given semantic specification conforms to predefined standards. It verifies each key in predefined dictionary. If the check fails, it logs the error and returns ``NONUSABLE_LEARNWARE`` status and error message.
-
-EasyStatChecker Class
----------------------
-This checker checks the statistical specification and functionality of a learnware object. It performs multiple checks to validate the learnware. It checks for model instantiation, verifies input shape and statistical specifications, and test output shape using random generated data. In case of any exceptions, it logs the error and returns ``NONUSABLE_LEARNWARE`` status and error message.