Merge pull request #6 from ZanMervic/docs

As Fairness Data widget documentation fix

doc/index.rst

@@ -7,7 +7,7 @@ Widgets
 .. toctree::
    :maxdepth: 1
 
-   widgets/as-fairness
+   widgets/as-fairness-data
    widgets/dataset-bias
    widgets/reweighing
    widgets/adversarial-debiasing

doc/widgets.json

@@ -3,9 +3,9 @@
         "Fairness",
         [
             {
-                "text": "As Fairness",
-                "doc": "widgets/as-fairness.md",
-                "icon": "../orangecontrib/fairness/widgets/icons/as_fairness.svg",
+                "text": "As Fairness Data",
+                "doc": "widgets/as-fairness-data.md",
+                "icon": "../orangecontrib/fairness/widgets/icons/as_fairness_data.svg",
                 "keywords": [
                     "fairness"
                 ]

doc/widgets/as-fairness-data.md

@@ -0,0 +1,24 @@
+As Fairness Data
+================
+Adds Fairness attributes to the dataset.
+
+**Inputs**
+
+- Data: reference dataset
+
+**Outputs**
+
+- Data: reference dataset with selected Fairness meta-attributes.
+
+**As Fairness Data** is a widget that allows the user to select and add Fairness meta-attributes to the dataset. These meta-attributes are required by other fairness widget to function properly. These meta-attributes are: `Favorable Class Value`, `Protected Attributes` and `Privileged Protected Attribute Values`.
+
+![](images/as-fairness-data.png)
+
+Example
+-------
+
+This simple example shows how to use the **As Fairness Data** widget. First, we load the German Credit Data dataset and then we connect it to the **As Fairness Data** widget. We select the `Personal status and sex` attribute as the `Protected Attribute` and all values representing males as the `Privileged Protected Attribute Values`. We also select `good` as the `Favorable Class Value`. The output of the widget is a dataset with the added meta-attributes.
+
+![](images/as-fairness-data-example.png)
+
+It should be noted that datasets included in orange with the tag `fairness` already have some default fairness meta-attributes added to them and thus do not require the use of the **As Fairness Data** widget before using other fairness widgets. These datasets include: `Adult`, `German Credit Data` and `COMPAS Analysis`.

doc/widgets/dataset-bias.md

@@ -19,6 +19,6 @@ This example shows a very simple use of the **Dataset Bias** widget. First we lo
 
 ![](images/dataset-bias-example.png)
 
-Note, we did not use the [As Fairness Data](as-fairness.md) widget before using the **Dataset Bias** widget. This is because the Adult dataset already has the required fairness meta-attributes added to it.
+Note, we did not use the [As Fairness Data](as-fairness-data.md) widget before using the **Dataset Bias** widget. This is because the Adult dataset already has the required fairness meta-attributes added to it.
 
 Another thing to note is that the **Dataset Bias** widget (and other fairness widgets) do not support missing values, this is indicated by the warning icon above the widget. Any missing values in the dataset will automatically be imputed with the average or most frequent value before being used by the widget.

orangecontrib/fairness/widgets/owasfairness.py

@@ -18,7 +18,7 @@ class OWAsFairness(OWWidget):
     """
     name = "As Fairness Data"
     description = "Converts a dataset to a fairness dataset with marked favorable class values, protected attributes and priviliged protected attribute values."
-    icon = 'icons/as_fairness.svg'
+    icon = 'icons/as_fairness_data.svg'
     priority = 0
 
     want_main_area = False