Updated readme with module file guidelines and example

tfx_addons/feature_selection/README.md

@@ -19,13 +19,45 @@ Component
 This project will allow the user to select different algorithms for performing feature selection on datasets artifacts in TFX pipelines.
 
 ## Project Implementation
-Feature Selection Custom Component will be implemented as Python function-based component.
-Implementation of the Feature Selection Custom Component can be done using the following approach:
+Feature Selection Custom Component is implemented as Python function-based component.
+
+Implementation of the Feature Selection Custom Component is done using the following approach:
 - Get dataset artifact generated by ExampleGen
-- Convert it into the format compatible with Scikit-Learn functions
-- Perform univariate feature selection using parameters given by users
-- Remove not selected features from the dataset
-- Provide feature scores of the selected features as a custom artifact
+- Convert it into the format compatible with Scikit-Learn functions (TFRecord to numpy disctionaries)
+- Perform univariate feature selection with `SelectorFunc` specified in the module file
+- Output the following two artifacts:
+    - `updated_data`: Duplicate of the input `Example` artifact, but with updated URI and data values
+    - `feature_selection`: Contains data about the feature selection process with the following values available:
+        - `scores`: Metric scores from the selector
+        - `p_values`: Calculated p-values from the selector
+        - `selected_features`: List of selected columns afetr feature selection
+
+## Module file
+#### Structure
+The module file is required to have a structure with the following three values:
+- `SELECTOR_PARAMS`: Parameters for `SelectorFunc`
+- `TARGET_FEATURE`: The target feature in the dataset
+- `SelectorFunc`: Univariate function for feature selection
+
+#### Example module file
+In the below example, we have used sklearn functions directly for simplicity. You may define custom functions while ensuring that the overall i/o structure is the same.
+``` python
+from sklearn.feature_selection import SelectKBest as SelectorFunc
+from sklearn.feature_selection import chi2
+
+SELECTOR_PARAMS = {"score_func": chi2, "k": 2}
+TARGET_FEATURE = 'species'
+```
+
+## Example usage
+You may use the feature selection component in a way similar to [StatisticsGen](https://www.tensorflow.org/tfx/guide/statsgen)
+``` python
+feature_selector = FeatureSelection(
+    orig_examples = example_gen.outputs['examples'],
+    module_file='example.modules.iris_module_file'
+    )
+```
+
 
 ## Project Dependencies
 The implementation will use the [Scikit-learn feature selection functions](https://scikit-learn.org/stable/modules/feature_selection.html)