Merge branch 'master' of https://github.com/oegedijk/explainerdashboard

.vscode/settings.json

@@ -1,5 +1,5 @@
 {
-    "python.pythonPath": "C:\\ProgramData\\Anaconda3\\envs\\ww_env2\\python.exe",
+    "python.pythonPath": "/anaconda/bin/python",
     "python.testing.unittestArgs": [
         "-v",
         "-s",

TODO.md

@@ -16,17 +16,20 @@
 ## Plots:
 - add multiclass confusion matrix
 - individual trees: highlight selected tree
+- Add feature names to waterfall plot
+- fix percentages difference bug lift plot vs classification plot
 
 ### Regression plots:
 
 ## Explainers:
+- add `get_metrics_dict()` function
 
 ## Dashboard:
-- add dependence plot to importances list
-- Contributions: add div margin to index selector
-- move number of features to display
+- FIX NOT LOADING UNTIL CLICKED ON CONTRIBUTIONS TAB BUG!
 - add option for vertical contributions?
 - reformat contributions table
+- add final prediction to contributions table
+- 
 
 ## Methods:
 
@@ -36,12 +39,11 @@
 
 
 ## Library level:
-- fix forever updating bug (seems shadow tree related?)
 - fix jupyter reload pdp bug
 - submit pull request to dtreeviz to accept shadowtree as parameter
 - just add shap='tree', 'linear', 'deep', etc instead of separate classes
 - add long description to pypi: https://packaging.python.org/guides/making-a-pypi-friendly-readme/
-- Add tests
+- Add more tests!
 - Test with lightgbm, catboost, extratrees
 - turn all docstrings into sphynx compatible
 - Fix numpy mock import in readthedocs compile

dashboard_examples.ipynb

@@ -9,12 +9,17 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
    "execution_count": 1,
+=======
+   "execution_count": null,
+>>>>>>> 5c623f400e54fea25a04f60bcc9229b0cc2a17d8
    "metadata": {
     "ExecuteTime": {
      "end_time": "2019-11-06T13:04:08.094300Z",
      "start_time": "2019-11-06T13:04:07.767281Z"
-    }
+    },
+    "collapsed": false
    },
    "outputs": [],
    "source": [
@@ -24,12 +29,17 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
    "execution_count": 2,
+=======
+   "execution_count": null,
+>>>>>>> 5c623f400e54fea25a04f60bcc9229b0cc2a17d8
    "metadata": {
     "ExecuteTime": {
      "end_time": "2019-11-06T13:04:25.772439Z",
      "start_time": "2019-11-06T13:04:08.103302Z"
-    }
+    },
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -41,11 +51,16 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
    "execution_count": 3,
+=======
+   "execution_count": null,
+>>>>>>> 5c623f400e54fea25a04f60bcc9229b0cc2a17d8
    "metadata": {
     "ExecuteTime": {
      "start_time": "2019-11-06T13:03:59.697Z"
-    }
+    },
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -63,12 +78,21 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
    "execution_count": 4,
+=======
+   "execution_count": null,
+>>>>>>> 5c623f400e54fea25a04f60bcc9229b0cc2a17d8
    "metadata": {
     "ExecuteTime": {
      "end_time": "2019-11-06T13:03:52.617314Z",
      "start_time": "2019-11-06T08:38:37.677784Z"
+<<<<<<< HEAD
     }
+=======
+    },
+    "collapsed": false
+>>>>>>> 5c623f400e54fea25a04f60bcc9229b0cc2a17d8
    },
    "outputs": [],
    "source": [
@@ -86,6 +110,7 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
    "execution_count": 19,
    "metadata": {},
    "outputs": [
@@ -109,6 +134,13 @@
      ]
     }
    ],
+=======
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+>>>>>>> 5c623f400e54fea25a04f60bcc9229b0cc2a17d8
    "source": [
     "db = ExplainerDashboard(explainer,\n",
     "                        model_summary=True,\n",
@@ -117,6 +149,7 @@
     "                        shap_interaction=True,\n",
     "                        shadow_trees=True)\n",
     "db.run(8052)"
+<<<<<<< HEAD
    ]
   },
   {
@@ -137,6 +170,8 @@
    ],
    "source": [
     "str(type(explainer.model)).endswith(\"RandomForestClassifier'>\")"
+=======
+>>>>>>> 5c623f400e54fea25a04f60bcc9229b0cc2a17d8
    ]
   },
   {
@@ -149,7 +184,13 @@
   {
    "cell_type": "code",
    "execution_count": null,
+<<<<<<< HEAD
    "metadata": {},
+=======
+   "metadata": {
+    "collapsed": true
+   },
+>>>>>>> 5c623f400e54fea25a04f60bcc9229b0cc2a17d8
    "outputs": [],
    "source": [
     "X_train, y_train, X_test, y_test = titanic_fare()\n",
@@ -166,7 +207,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "db = ExplainerDashboard(explainer,\n",
@@ -192,7 +235,8 @@
     "ExecuteTime": {
      "end_time": "2019-11-05T15:00:25.680557Z",
      "start_time": "2019-11-05T12:20:30.468610Z"
-    }
+    },
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -218,7 +262,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": []
   }
@@ -239,7 +285,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.5"
+   "version": "3.6.9"
   }
  },
  "nbformat": 4,