learnware/docs/workflow/identify.rst

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Helpful Learnwares Identification
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When a user comes with her requirements, the market should identify helpful learnwares and recommend them to the user.
The search of helpful learnwares is based on the user information, and can be divided into two stages: semantic specification search and statistical specification search.

User information
-------------------------------
The user should provide her requirements in ``BaseUserInfo``. The class ``BaseUserInfo`` consists of user's semantic specification ``user_semantic`` and statistical information ``stat_info``. 

The semantic specification ``user_semantic`` is stored in a ``dict``, with keywords 'Data', 'Task', 'Library', 'Scenario', 'Description' and 'Name'. An example is shown below, and you could choose their values according to the figure. For the keys of type 'Class, you should choose one ilegal value; for the keys of type 'Tag', you can choose one or more values; for the keys of type 'String', you should provide a string; the key 'Description' is used in learnwares' semantic specifications and is ignored in user semantic specification; the values of all these keys can be empty if the user have no idea of them.

.. code-block:: python

    # An example of user_semantic
    user_semantic = {
        "Data": {"Values": ["Image"], "Type": "Class"},
        "Task": {"Values": ["Classification"], "Type": "Class"},
        "Library": {"Values": ["Scikit-learn"], "Type": "Tag"},
        "Scenario": {"Values": ["Education"], "Type": "Class"},
        "Description": {"Values": "", "Type": "String"},
        "Name": {"Values": "digits", "Type": "String"},
    }


.. image:: ../_static/img/semantic_spec.png
   :align: center


The user's statistical information ``stat_info`` is stored in a ``json`` file, e.g., ``stat.json``. The generation of this file is seen in `Learnware Preparation <./submit>`_.



Semantic Specification Search
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To search for learnwares that fit your task purpose, 
the user should first provide a semantic specification ``user_semantic`` that describes the characteristics of your task.
The Learnware Market will perform a first-stage search based on ``user_semantic``,
identifying potentially helpful leaarnwares whose models solve tasks similar to your requirements. 

.. code-block:: python

    # construct user_info which includes semantic specification for searching learnware
    user_info = BaseUserInfo(semantic_spec=semantic_spec)

    # search_learnware performs semantic specification search if user_info doesn't include a statistical specification
    _, single_learnware_list, _ = easy_market.search_learnware(user_info) 

    # single_learnware_list is the learnware list by semantic specification searching
    print(single_learnware_list)

In semantic specification search, we go through all learnwares in the market to compare their semantic specifications with the user's one, and return all the learnwares that pass through the comparation. When comparing two learnwares' semantic specifications, we design different ways for different semantic keys:

- For semantic keys with type 'Class', they are matched only if they have the same value.

- For semantic keys with type 'Tag', they are matched only if they have nonempty intersections.

- For the user's input in the search box, it matchs with a learnware's semantic specification only if it's a substring of its 'Name' or 'Description'. All the strings are converted to the lower case before matching.

- When a key value is missing, it will not participate in the match. The user could upload no semantic specifications if he wants.

Statistical Specification Search
---------------------------------

If you choose to provide your own statistical specification file ``stat.json``, 
the Learnware Market can perform a more accurate leanware selection from 
the learnwares returned by the previous step. This second-stage search is based on statistical information and returns one or more learnwares that are most likely to be helpful for your task. 

For example, the following code is designed to work with Reduced Kernel Mean Embedding (RKME) as a statistical specification:

.. code-block:: python

    import learnware.specification as specification

    user_spec = specification.rkme.RKMEStatSpecification()
    user_spec.load(os.path.join("rkme.json"))
    user_info = BaseUserInfo(
        semantic_spec=user_semantic, stat_info={"RKMEStatSpecification": user_spec}
    )
    (sorted_score_list, single_learnware_list,
        mixture_score, mixture_learnware_list) = easy_market.search_learnware(user_info)

    # sorted_score_list is the learnware scores based on MMD distances, sorted in descending order
    print(sorted_score_list) 

    # single_learnware_list is the learnwares sorted in descending order based on their scores
    print(single_learnware_list)

    # mixture_learnware_list is the learnwares whose mixture is helpful for your task
    print(mixture_learnware_list) 

    # mixture_score is the score of the mixture of learnwares
    print(mixture_score)

The return values of statistical specification search are ``sorted_score_list``, ``single_learnware_list``, ``mixture_score`` and ``mixture_learnware_list``.
``sorted_score_list`` and ``single_learnware_list`` are the ranking of each single learnware and the corresponding scores. We return at least 15 learnwares unless there're no enough ones. If there are more than 15 matched learnwares, the ones with scores less than 50 will be ignored.
``mixture_score`` and ``mixture_learnware_list`` are the chosen mixture learnwares and the corresponding score. At most 5 learnwares will be chosen, whose mixture may have a relatively good performance on the user's task.


The statistical specification search is done in the following way.
We first filter by the dimension of RKME specifications; only those with the same dimension with the user's will enter the subsequent stage.

The single_learnware_list is calculated using the distances between two RKMEs. The greater the distance from the user's RKME, the lower the score is. 

The mixture_learnware_list is calculated in a greedy way. Each time we choose a learnware to make their mixture closer to the user's RKME. Specifically, each time we go through all the left learnwares to find the one whose combination with chosen learnwares could minimize the distance between their mixture's RKME and the user's RKME. The mixture weight is calculated by minimizing the RKME distance, which is solved by quadratic programming. If the distance become larger or the number of chosen learnwares reaches a threshold, the process will end and the chosen learnware and weight list will return.