Merge pull request #20 from rcrowe-google/Pratishtha/base/README

updated README with usage instructions

README.md

@@ -1,8 +1,83 @@
 # Schema Curation Custom Component
 
-> Outreachy TFX custom component project
+[![Python](https://img.shields.io/pypi/pyversions/tfx.svg?style=plastic)](https://github.com/tensorflow/tfx)
+[![TensorFlow](https://img.shields.io/badge/TFX-orange)](https://www.tensorflow.org/tfx)
 
-This repo contains the code for Schema Curation Custom Component made as a part of [TFX-Addons](https://github.com/tensorflow/tfx-addons/) through the [Outreachy](https://www.outreachy.org/outreachy-may-2021-internship-round/communities/tensorflow/#create-custom-components-and-tools-for-tensorflow-) program. You may view the linked Pull Request in TFX-Addons [here](https://github.com/tensorflow/tfx-addons/pull/32) and the issue [here](https://github.com/tensorflow/tfx-addons/issues/8) for relevant discussions related to the project.
+This is a TFX-component that allows its users to apply a user code to a schema produced by the [SchemaGen](https://www.tensorflow.org/tfx/guide/schemagen) component, and curate it based on domain knowledge. It fits seamlessly into the ML-pipline made with TFX, and allows schema manipulation based on a module file provided by the User.
+
+## Documentation
+
+### Inputs:
+The custom component takes for input the user *module file*, and the *schema* generated by the SchemaGen component on the specified data.
+
+### Output:
+On running the component, it outputs the *modified schema* based on the code provided in the module file.
+
+## Module file
+
+### The Schema Curation *schema_fn*:
+The Schema Curation component provides a solution to curating the schema based on user knowledge. As a user, you only have to define a single function called the `schema_fn`. in `schema_fn` you define a series of funcitons that manipulate the input schema to produce the required one. 
+
+An example is:
+
+```
+def schema_fn(schema):
+  """modifies the infered schema.
+  Args:
+    schema:schema generated by SchemaGen component of tfx
+  """
+  #changing "tips" into optional feature
+  feature = tfdv.get_feature(schema, 'tips') 
+  feature.presence.min_fraction = 0.9
+  
+  return schema
+```
+
+## Project Structure
+
+### Directory Structure
+```
+schemacomponent
+├── component
+│   ├── component.py
+│   ├── component_test.py
+│   ├── executor.py
+│   ├── __init__.py
+│   └── __pycache__
+│       ├── component.cpython-38.pyc
+│       └── executor.cpython-38.pyc
+├── CONTRIBUTING.md
+├── data
+│   └── data.csv
+├── example
+│   ├── __init__.py
+│   ├── module_file.py
+│   ├── taxi_example_colab.ipynb
+│   ├── taxi_example_local.py
+│   ├── taxi_pipeline_hello_e2e_test.py
+│   └── taxi_pipeline_hello.py
+├── __init__.py
+├── PROPOSAL.md
+└── README.md
+```
+
+
+The project follows the structure specified by the [TFX](https://www.tensorflow.org/tfx) documentation for a [TFX fully custom component](https://www.tensorflow.org/tfx/guide/custom_component). 
+
+The `SchemaCurationSpec` class defines the input, output and execution parameters required by the component.
+
+The `Executor` class defines the functioning of the component, a subclass of the `base_executor.BaseExecutor` with the overriden `Do` function.
+
+Finally the `SchemaCuration` class integrates the fully custom component into the ML pipeline.
+
+### Unit Tests
+
+The component includes separate unit tests for the component and the executor. 
+
+
+## Credits
+
+Schema Curation Custom Component was made as a part of [TFX-Addons](https://github.com/tensorflow/tfx-addons/) through the [Outreachy](https://www.outreachy.org/outreachy-may-2021-internship-round/communities/tensorflow/#create-custom-components-and-tools-for-tensorflow-) program. You may view the linked Pull Request in TFX-Addons [here](https://github.com/tensorflow/tfx-addons/pull/32) and the issue [here](https://github.com/tensorflow/tfx-addons/issues/8) for relevant discussions related to the project.
 
 ## The Team:
 ### Mentors: