[DOC] Modify docs

docs/index.rst

@@ -23,7 +23,9 @@ Document Structure
    :maxdepth: 3
    :caption: GETTING STARTED:
 
-   Introduction <introduction/intro.rst>
+   Introduction <start/intro.rst>
+   Quick Start <start/quick.rst>
+   Installation Guide <start/install.rst>
 
 .. toctree::
    :maxdepth: 3

docs/introduction/quick.rst

@@ -1,170 +0,0 @@
-.. _quick:
-============================================================
-Quick Start
-============================================================
-
-
-Introduction
-====================
-
-This ``Quick Start`` guide tries to demonstrate
-
-- It's very easy to build a complete Learnware Market workflow and use ``learnware`` to deal with users' tasks.
-
-
-Installation
-====================
-
-Learnware is currently hosted on `PyPI <https://pypi.org/>`__. You can easily intsall ``learnware`` according to the following steps:
-
-- For Windows and Linux users:
-
-    .. code-block::
-
-        pip install learnware
-
-- For macOS users:
-
-    .. code-block::
-
-        conda install -c pytorch fais
-        pip install learnware
-
-
-Prepare Learnware
-====================
-
-The Learnware Market consists of a wide range of learnwares. A valid learnware is a zip file which 
-is composed of the following four parts. Please refer to
-:ref:`script` for examples of these components.
-
-- ``__init__.py``
-
-    A python file offering interfaces for your model's fitting, predicting and fine-tuning.
-
-- ``rkme.json``
-
-    A json file containing the statistical specification of your data. 
-
-- ``learnware.yaml``
-    
-    A config file describing your model class name, type of statistical specification(e.g. Reduced Kernel Mean Embedding, ``RKMEStatSpecification``), and 
-    the file name of your statistical specification file.
-
-- ``environment.yaml``
-
-    A Conda environment configuration file for running the model (if the model environment is incompatible, you can rely on this for manual configuration). 
-    You can generate this file according to the following steps:
-
-    - Create env config for conda:
-
-        .. code-block::
-
-            conda env export | grep -v "^prefix: " > environment.yml
-        
-    - Recover env from config:
-
-        .. code-block::
-
-            conda env create -f environment.yml
-
-
-Learnware Market Workflow
-============================
-
-Users can start an Learnware Market workflow according to the following steps:
-
-1. Initialize a Learware Market:
-
-    .. code-block:: python
-        
-        import learnware
-        from learnware.market import EasyMarket
-
-        learnware.init()
-        easy_market = EasyMarket(market_id="demo", rebuild=True)
-
-2. Upload leanware:
-
-    Here, ``zip_path`` is the directory of your learnware zip file.
-
-    .. code-block:: python
-
-        semantic_spec = {
-            "Data": {"Values": ["Tabular"], "Type": "Class"},
-            "Task": {"Values": ["Classification"], "Type": "Class"},
-            "Library": {"Values": ["Scikit-learn"], "Type": "Class"},
-            "Scenario": {"Values": ["Business"], "Type": "Tag"},
-            "Description": {"Values": "", "Type": "String"},
-            "Name": {"Values": "learnware_1", "Type": "String"},
-        }
-        semantic_spec["Name"]["Values"] = "learnware_user"
-        semantic_spec["Description"]["Values"] = "test_learnware_user" 
-        easy_market.add_learnware(zip_path, semantic_spec) 
-
-3. Semantic specification search:
-
-    The Learnware Market will perform first-step searching based on the semantic specification 
-    ``semantic_spec`` you provided. 
-    This searching process will indentify potentially helpful leranwares whose models
-    solve tasks similar to your requirements.
-
-    .. code-block:: python
-
-        user_semantic = {
-            "Data": {"Values": ["Tabular"], "Type": "Class"},
-            "Task": {
-                "Values": ["Classification"],
-                "Type": "Class",
-            },
-            "Library": {"Values": ["Scikit-learn"], "Type": "Tag"},
-            "Scenario": {"Values": ["Business"], "Type": "Class"},
-            "Description": {"Values": "", "Type": "String"},
-            "Name": {"Values": "", "Type": "String"},
-        }
-        user_info = BaseUserInfo(id="user", semantic_spec=user_semantic)
-        _, single_learnware_list, _ = easy_market.search_learnware(user_info)
-
-4. Statistical specification search:
-
-    If you choose to porvide your own statistical specification file ``rkme.json``, 
-    the Learnware Market can perform a more accurate leanware selection from 
-    the learnwares returned by the previous step. This second-step searching is carried out 
-    at the level of data distribution information and returns 
-    one or more learnwares that are most likely to be helpful for your task.
-
-    Here, ``unzip_path`` is the directory where you unzip your learnware file.
-
-    .. code-block:: python
-
-        import learnware.specification as specification
-
-        user_spec = specification.rkme.RKMEStatSpecification()
-        user_spec.load(os.path.join(unzip_path, "rkme.json"))
-        user_info = BaseUserInfo(
-            id="user", 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)
-
-5. Reuse learnwares:
-
-    Based on the returned list of learnwares ``mixture_learnware_list`` in the previous step, 
-    you can easily reuse them to make predictions your own data, instead of training a model from scratch. 
-    We provide two baseline methods for reusing a given list of learnwares, namely ``JobSelectorReuser`` and ``AveragingReuser``.
-
-    .. code-block:: python
-
-        reuse_job_selector = JobSelectorReuser(learnware_list=mixture_learnware_list)
-        job_selector_predict_y = reuse_job_selector.predict(user_data=test_x)
-
-        reuse_ensemble = AveragingReuser(learnware_list=mixture_learnware_list, mode='vote')
-        ensemble_predict_y = reuse_ensemble.predict(user_data=test_x)
-
-.. _script:
-
-Example: Learnware Files
--------
-
-Below is an example learnware that includes an SVM model and uses Reduced Kernel Mean Embedding as its statistical reduction method. 
-We have listed the files that it needs to include.

docs/start/install.rst

@@ -0,0 +1,3 @@
+===================
+Installation Guide
+===================

docs/start/quick.rst

@@ -0,0 +1,166 @@
+.. _quick:
+============================================================
+Quick Start
+============================================================
+
+
+Introduction
+====================
+
+This ``Quick Start`` guide tries to demonstrate
+
+- It's very easy to build a complete Learnware Market workflow and use ``learnware`` to deal with users' tasks.
+
+
+Installation
+====================
+
+Learnware is currently hosted on `PyPI <https://pypi.org/>`__. You can easily intsall ``learnware`` according to the following steps:
+
+- For Windows and Linux users:
+
+    .. code-block::
+
+        pip install learnware
+
+- For macOS users:
+
+    .. code-block::
+
+        conda install -c pytorch fais
+        pip install learnware
+
+
+Prepare Learnware
+====================
+
+The Learnware Market consists of a wide range of learnwares. A valid learnware is a zip file which 
+is composed of the following four parts.
+
+- ``__init__.py``
+
+    A python file offering interfaces for your model's fitting, predicting and fine-tuning.
+
+- ``rkme.json``
+
+    A json file containing the statistical specification of your data. 
+
+- ``learnware.yaml``
+    
+    A config file describing your model class name, type of statistical specification(e.g. Reduced Kernel Mean Embedding, ``RKMEStatSpecification``), and 
+    the file name of your statistical specification file.
+
+- ``environment.yaml``
+
+    A Conda environment configuration file for running the model (if the model environment is incompatible, you can rely on this for manual configuration). 
+    You can generate this file according to the following steps:
+
+    - Create env config for conda:
+
+        .. code-block::
+
+            conda env export | grep -v "^prefix: " > environment.yml
+        
+    - Recover env from config:
+
+        .. code-block::
+
+            conda env create -f environment.yml
+
+
+Learnware Market Workflow
+============================
+
+Users can start an Learnware Market workflow according to the following steps:
+
+Initialize a Learware Market
+-------------------------------
+
+.. code-block:: python
+    
+    import learnware
+    from learnware.market import EasyMarket
+
+    learnware.init()
+    easy_market = EasyMarket(market_id="demo", rebuild=True)
+
+Upload Leanwares
+-------------------------------
+
+Here, ``zip_path`` is the directory of your learnware zip file.
+
+.. code-block:: python
+
+    semantic_spec = {
+        "Data": {"Values": ["Tabular"], "Type": "Class"},
+        "Task": {"Values": ["Classification"], "Type": "Class"},
+        "Library": {"Values": ["Scikit-learn"], "Type": "Class"},
+        "Scenario": {"Values": ["Business"], "Type": "Tag"},
+        "Description": {"Values": "", "Type": "String"},
+        "Name": {"Values": "learnware_1", "Type": "String"},
+    }
+    semantic_spec["Name"]["Values"] = "learnware_user"
+    semantic_spec["Description"]["Values"] = "test_learnware_user" 
+    easy_market.add_learnware(zip_path, semantic_spec) 
+
+Semantic Specification Search
+-------------------------------
+
+The Learnware Market will perform first-step searching based on the semantic specification 
+``semantic_spec`` you provided. 
+This searching process will indentify potentially helpful leranwares whose models
+solve tasks similar to your requirements.
+
+.. code-block:: python
+
+    user_semantic = {
+        "Data": {"Values": ["Tabular"], "Type": "Class"},
+        "Task": {
+            "Values": ["Classification"],
+            "Type": "Class",
+        },
+        "Library": {"Values": ["Scikit-learn"], "Type": "Tag"},
+        "Scenario": {"Values": ["Business"], "Type": "Class"},
+        "Description": {"Values": "", "Type": "String"},
+        "Name": {"Values": "", "Type": "String"},
+    }
+    user_info = BaseUserInfo(id="user", semantic_spec=user_semantic)
+    _, single_learnware_list, _ = easy_market.search_learnware(user_info)
+
+Statistical Specification Search
+---------------------------------
+
+If you choose to porvide your own statistical specification file ``rkme.json``, 
+the Learnware Market can perform a more accurate leanware selection from 
+the learnwares returned by the previous step. This second-step searching is carried out 
+at the level of data distribution information and returns 
+one or more learnwares that are most likely to be helpful for your task.
+
+Here, ``unzip_path`` is the directory where you unzip your learnware file.
+
+.. code-block:: python
+
+    import learnware.specification as specification
+
+    user_spec = specification.rkme.RKMEStatSpecification()
+    user_spec.load(os.path.join(unzip_path, "rkme.json"))
+    user_info = BaseUserInfo(
+        id="user", 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)
+
+Reuse Learnwares
+-------------------------------
+
+Based on the returned list of learnwares ``mixture_learnware_list`` in the previous step, 
+you can easily reuse them to make predictions your own data, instead of training a model from scratch. 
+We provide two baseline methods for reusing a given list of learnwares, namely ``JobSelectorReuser`` and ``AveragingReuser``.
+
+.. code-block:: python
+
+    reuse_job_selector = JobSelectorReuser(learnware_list=mixture_learnware_list)
+    job_selector_predict_y = reuse_job_selector.predict(user_data=test_x)
+
+    reuse_ensemble = AveragingReuser(learnware_list=mixture_learnware_list, mode='vote')
+    ensemble_predict_y = reuse_ensemble.predict(user_data=test_x)

setup.py

@@ -51,7 +51,7 @@ def get_platform():
 # What packages are required for this module to be executed?
 # `estimator` may depend on other packages. In order to reduce dependencies, it is not written here.
 REQUIRED = [
-    "numpy>=1.20.0",
+    "numpy>=1.19.2",
     "pandas>=0.25.1",
     "scipy>=1.0.0",
     "matplotlib>=3.1.3",