bump version update release notes

RELEASE_NOTES.md

@@ -1,5 +1,14 @@
 # Release Notes
 
+## Version 0.2.10:
+
+### New Features
+- Explainer parameter `cats` now takes dicts as well where you can specify
+    your own groups of onehotencoded columns.
+        - e.g. instead of passing `cats=['Sex']` to group `['Sex_female', 'Sex_male', 'Sex_nan']`
+        you can now do this explicitly: `sex={'Gender'=['Sex_female', 'Sex_male', 'Sex_nan']}`
+        - Or combine the two: 
+            `cats=[{'Gender'=['Sex_female', 'Sex_male', 'Sex_nan']}, 'Deck', 'Embarked']`
 
 
 ## Version 0.2.9:

TODO.md

@@ -39,6 +39,7 @@
 ## notebooks:
 
 ## Dashboard:
+- organize explainer components according to tab
 - Add EDA style feature histograms, bar charts, correlation graphs, etc
 - add cost calculator/optimizer for classifier models based on confusion matrix weights
 - add group fairness metrics
@@ -50,6 +51,7 @@
 - add pos_label_name property to PosLabelConnector search
 - add "number of indexes" indicator to RandomIndexComponents for current restrictions
 - whatif component: check non duplicate feature names
+- set equivalent_col when toggling cats in dependence/interactions
 
 ## Methods:
 - Add LIME values
@@ -65,13 +67,18 @@
 - write tests for explainer_plots
 
 ## Docs:
-- Add type hints throughout library
+- Add type hints:
+    - to explainers
+    - to explainer class methods
+    - to explainer_methods
+    - to explainer_plots
 - Add pydate video: https://www.youtube.com/watch?v=1nMlfrDvwc8
 - document PosLabelSelector and PosLabelConnector, e.g.:
         self.connector = PosLabelConnector(self.roc_auc, self)
         self.register_components(self.connector)
 
 ## Library level:
+- hide (add '_') to non-api class methods
 - move dashboard_methods to root dir
 - build release on conda-forge
 - launch gunicorn server from python:

explainerdashboard/dashboard_components/overview_components.py

@@ -426,8 +426,8 @@ def __init__(self, explainer, title="What if...", name=None,
     def _generate_dash_input(self, col, cats, cats_dict):
         if col in cats:
             col_values = [
-                col[len(col)+1:] if col.startswith(col+"_") else col 
-                    for col in cats_dict[col]]
+                col_val[len(col)+1:] if col_val.startswith(col+"_") else col_val
+                    for col_val in cats_dict[col]]
             return html.Div([
                 html.P(col),
                 dcc.Dropdown(id='whatif-'+col+'-input-'+self.name, 

setup.py

@@ -2,7 +2,7 @@
 
 setup(
     name='explainerdashboard',
-    version='0.2.9',
+    version='0.2.10',
     description='explainerdashboard allows you quickly build an interactive dashboard to explain the inner workings of your machine learning model.',
     long_description="""
 
@@ -18,15 +18,15 @@
 
     - Make it easy for data scientists to quickly inspect the inner workings and 
         performance of their model with just a few lines of code
-    - Make it possible for non data scientist stakeholders such as managers, 
-        directors, internal and external watchdogs to interactively inspect 
-        the inner workings of the model without having to depend on a data 
-        scientist to generate every plot and table
+    - Make it possible for non data scientist stakeholders such as co-workers,
+        managers, directors, internal and external watchdogs to interactively 
+        inspect the inner workings of the model without having to depend 
+        on a data scientist to generate every plot and table
     - Make it easy to build a custom application that explains individual 
         predictions of your model for customers that ask for an explanation
     - Explain the inner workings of the model to the people working with 
         model in a human-in-the-loop deployment so that they gain understanding 
-        what the model does and doesn't do. 
+        what the model does do and does not do. 
         This is important so that they can gain an intuition for when the 
         model is likely missing information and may have to be overruled.