added class documentation

Author: KshitizLohia

ads/feature_store/docs/requirements.txt

@@ -6,7 +6,7 @@ sphinx_copybutton
 sphinx_code_tabs
 sphinx-autobuild
 sphinx-autorun
-oracle_ads
+oracle_ads==2.9.0rc0
 furo
 IPython
 pandoc

ads/feature_store/docs/source/feature_group.rst

@@ -220,6 +220,7 @@ Use the ``get_validation_output()`` method of the ``FeatureGroup`` instance to f
 The ``get_validation_output()`` method has the following optional parameter:
 
 - ``job_id: string``. ID of the feature group job.
+
 ``get_validation_output().to_pandas()`` Outputs  the validation results for each expectation as Pandas dataframe.
 
 .. image:: figures/validation_results.png

ads/feature_store/docs/source/feature_store.rst

@@ -99,7 +99,7 @@ The following example queries a feature store using SQL:
   df = feature_store.sql(sql)
 
 Create an Entity
-=============
+================
 Use the ``create_entity()`` method of the ``FeatureStore`` instance to create an ``Entity``.
 
 .. code-block:: python3
@@ -108,7 +108,7 @@ Use the ``create_entity()`` method of the ``FeatureStore`` instance to create an
   feature_store.create_entity(name="<ENTITY_NAME>")
 
 Create a Transformation
-=====================
+=======================
 Transformations in a feature store are the operations and processes applied to raw data to create, modify, or derive new features for use as inputs for machine learning models. These transformations are necessary for improving the quality, relevance, and usefulness of features that then enhance the performance of models.
 You can call the ``create_transformation()`` method of the FeatureStore instance to create a ``Transformation``.
 

ads/feature_store/docs/source/index.rst

@@ -44,6 +44,7 @@ Feature Store
 
     statistics
     feature_validation
+    data_versioning
     ui
 
 .. toctree::
@@ -59,3 +60,15 @@ Feature Store
     :caption: Release notes:
 
     release_notes
+
+.. toctree::
+    :hidden:
+    :maxdepth: 5
+    :caption: Classes:
+
+    module
+
+License
++++++++
+
+Copyright (c) 2020, 2022 Oracle and/or its affiliates. Licensed under the `Universal Permissive License v1.0 <https://oss.oracle.com/licenses/upl/>`_

ads/feature_store/docs/source/module.rst

@@ -0,0 +1,83 @@
+===================
+Class Documentation
+===================
+
+ads.feature_store package
+=========================
+
+.. automodule:: ads.feature_store.feature_group
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+.. automodule:: ads.feature_store.feature_group_job
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+.. automodule:: ads.feature_store.dataset
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+.. automodule:: ads.feature_store.dataset_job
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+.. automodule:: ads.feature_store.transformation
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+.. automodule:: ads.feature_store.statistics_config
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+.. automodule:: ads.feature_store.model_details
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+.. automodule:: ads.feature_store.input_feature_detail
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+.. automodule:: ads.feature_store.feature
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+.. automodule:: ads.feature_store.entity
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+
+.. automodule:: ads.feature_store.feature_group_expectation
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+ads.feature_store.query package
+===============================
+
+Submodules
+----------
+
+ads.feature_store.query.filter
+------------------------------
+
+.. automodule:: ads.feature_store.query.filter
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+ads.feature_store.query.join
+------------------------------
+.. automodule:: ads.feature_store.query.join
+   :members:
+   :undoc-members:
+   :show-inheritance:

ads/feature_store/docs/source/quickstart.rst

@@ -7,6 +7,7 @@ Quick start
   ..  code-block:: shell
 
      odsc conda install -s fspyspark32_p38_cpu_v1
+
 3. Download the notebook examples from the example notebook section.
 
 .. seealso::

ads/feature_store/docs/source/release_notes.rst

@@ -105,7 +105,7 @@ Release notes: July 5, 2023
 * [DOCS] Updated terraform default version as 1.1.x
 
 1.0.0
-----
+-----
 
 .. note::
 

ads/feature_store/docs/source/terraform.rst

@@ -1,6 +1,6 @@
-===================================
+=====================================
 Terraform: Setting Up a Feature Store
-===================================
+=====================================
 
 The following shows a terraform stack deployment of the feature store resources.
 
@@ -12,7 +12,7 @@ Blue-green deployment is a strategy for releasing new versions of an application
 .. _User Policies:
 
 Required Policies for a Terraform Stack
-=============================
+=======================================
 
 Feature store requires the following policy statements to deploy the feature store in a Terraform stack:
 
@@ -200,7 +200,7 @@ The following Terraform variables used in this stack are:
 
 
 User VCN Deployment
-===============================
+===================
 Bring your own database (BYODB) is not supported by feature store because it doesn't support private access using a private endpoint and private access gateway for ATP instances.
 
 You can specify VCN details for the feature store to use an existing VCN. A Terraform stack feature store has the following Terraform VCN variables:
@@ -249,7 +249,7 @@ You can specify VCN details for the feature store to use an existing VCN. A Terr
 
 
     Onboarding a Database
-    ##########
+    #####################
 
     Specify the following details to onboard database instancesL
 

ads/feature_store/docs/source/ui.rst

@@ -34,7 +34,7 @@ Statistical analysis of features helps uncover insights about their distribution
 .. image:: figures/stats_fg.png
 
 Dataset
-======
+=======
 Datasets also support iterative experimentation, allowing data scientists to create various dataset configurations with different feature combinations and transformations, which facilites the discovery of the most valuable features for model training.
 
 .. image:: figures/dataset.gif