Final cleanups and add video to description (#4)

* add video

* change readme

* add video

* add video

* add video

* add video

* add video

* add video

* add video

* add video

* update example

Author: CloseChoice

README.md

@@ -6,6 +6,10 @@
 
 xaiflow integrates seamlessly with MLflow to generate interactive HTML reports for SHAP analysis. Instead of static charts and images, you get rich, interactive visualizations that stakeholders can explore and understand.
 
+Here should the video go:
+[![xaiflow showcase](video/video_thumbnail.png)](https://github.com/user-attachments/assets/f508fa6f-ab0f-493d-a892-ed958331e30a)
+*Click the image above to watch the feature showcase video.*
+
 ## What We're Trying to Achieve
 
 Most ML workflows produce explanations as static images or basic charts, which creates several problems:
@@ -38,10 +42,9 @@ with mlflow.start_run():
 
     # Add interactive explainable AI reports
     plugin = XaiflowPlugin()
-    plugin.log_feature_importance_report(
+    plugin.log_xai_report(
         feature_names=X.columns.tolist(),
         shap_values=shap_values,
-        report_name="model_explanation.html"
     )
 ```
 
@@ -81,7 +84,7 @@ xaiflow/
 ### Core Components
 
 **MLflow Integration** (`mlflow_plugin.py`)
-The `CEMLflowPlugin` class handles the integration with MLflow. The main method `log_feature_importance_report()` processes SHAP values, manages feature encodings, and stores the generated reports as MLflow artifacts.
+The `CEMLflowPlugin` class handles the integration with MLflow. The main method `log_xai_report()` processes SHAP values, manages feature encodings, and stores the generated reports as MLflow artifacts.
 
 **Report Generation** (`report_generator.py`)
 The `ReportGenerator` class converts SHAP data into interactive HTML reports using Jinja2 templating. It handles template loading, asset bundling, and data injection into the frontend components.
@@ -127,11 +130,10 @@ feature_encodings = {
     'region': {0: 'North', 1: 'South', 2: 'East', 3: 'West'}
 }
 
-plugin.log_feature_importance_report(
+plugin.log_xai_report(
     feature_names=feature_names,
     shap_values=shap_values,
     feature_encodings=feature_encodings,
-    report_name="enhanced_report.html"
 )
 ```
 

examples/notebooks/auto_mpg_example.ipynb

@@ -2,15 +2,15 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "79de156f",
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/tobias/programming/cloudexplain/ce-mlflow-extension/ce-mlflow-extension/.venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "c:\\programming\\cloudexplain\\xflow\\.venv\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
       "  from .autonotebook import tqdm as notebook_tqdm\n"
      ]
     }
@@ -42,21 +42,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "0da5d7e2",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Loaded bundle.js content (218107 characters)\n",
-      "Saved report data to test_report_data.json\n",
-      "logged to test_report.html\n",
-      "Feature importance report logged to MLflow: reports/test_report_auto_mpg.html\n",
-      "Run ID: 72ea715bfe9c42bc840388933f6999a8. If you are running mlflow locally use:\n",
+      "Loaded bundle.js content (225719 characters)\n",
+      "Feature importance report logged to MLflow: reports/feature_importance_report.html\n",
+      "Run ID: 7521c3f260f84a5d8e038a13bc91498b. If you are running mlflow locally use:\n",
       "python -m mlflow ui --port 5000\n",
-      "Then open http://localhost:5000/#/experiments/921177506761828334/runs/72ea715bfe9c42bc840388933f6999a8 to view the report.\n"
+      "Then open http://localhost:5000/#/experiments/557047036753041520/runs/7521c3f260f84a5d8e038a13bc91498b to view the report. Note: it's important to start mlflow in the directory in which you execute the notebook.\n"
      ]
     }
    ],
@@ -91,10 +89,9 @@
     "    feature_encodings = {'cylinders_encoded': {0: '3', 1: '4', 2: '5', 3: '6', 4: '8'},\n",
     "                            'model_encoded': {0: 'Super 70', 1: 'Super 71', 2: 'Low 72', 3: 'Nice 73', 4: 'Great 74', 5: 'Lame 75', 6: 'High 76', 7: '77', 8: '78', 9: '79', 10: '80', 11: '81', 12: '82'},\n",
     "                            'origin_encoded': {0: 'Afghanistan', 1: 'Bangladesh', 2: 'Maui'}}\n",
-    "    artifact_path = plugin.log_feature_importance_report(\n",
+    "    artifact_path = plugin.log_xai_report(\n",
     "        feature_names=list(X.columns),\n",
     "        shap_values=shap_values,\n",
-    "        report_name=\"test_report_auto_mpg.html\",\n",
     "        feature_encodings=feature_encodings\n",
     "    )\n",
     "    run_id = mlflow.active_run().info.run_id\n",
@@ -119,7 +116,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.3"
+   "version": "3.13.5"
   }
  },
  "nbformat": 4,

examples/notebooks/azure_ml_auto_mpg_example.ipynb

@@ -258,10 +258,9 @@
         "    feature_encodings = {'cylinders_encoded': {0: '3', 1: '4', 2: '5', 3: '6', 4: '8'},\n",
         "                            'model_encoded': {0: 'Super 70', 1: 'Super 71', 2: 'Low 72', 3: 'Nice 73', 4: 'Great 74', 5: 'Lame 75', 6: 'High 76', 7: '77', 8: '78', 9: '79', 10: '80', 11: '81', 12: '82'},\n",
         "                            'origin_encoded': {0: 'Afghanistan', 1: 'Bangladesh', 2: 'Maui'}}\n",
-        "    artifact_path = plugin.log_feature_importance_report(\n",
+        "    artifact_path = plugin.log_xai_report(\n",
         "        feature_names=list(X.columns),\n",
         "        shap_values=shap_values,\n",
-        "        report_name=\"test_report_auto_mpg.html\",\n",
         "        feature_encodings=feature_encodings\n",
         "    )\n",
         "    run_id = mlflow.active_run().info.run_id\n",

examples/scripts/auto_mpg_example.py

@@ -43,11 +43,12 @@
     feature_encodings = {'cylinders_encoded': {0: '3', 1: '4', 2: '5', 3: '6', 4: '8'},
                             'model_encoded': {0: 'Super 70', 1: 'Super 71', 2: 'Low 72', 3: 'Nice 73', 4: 'Great 74', 5: 'Lame 75', 6: 'High 76', 7: '77', 8: '78', 9: '79', 10: '80', 11: '81', 12: '82'},
                             'origin_encoded': {0: 'Afghanistan', 1: 'Bangladesh', 2: 'Maui'}}
-    artifact_path = plugin.log_feature_importance_report(
+    artifact_path = plugin.log_xai_report(
         feature_names=list(X.columns),
         shap_values=shap_values,
-        report_name="test_report_auto_mpg.html",
-        feature_encodings=feature_encodings
+        feature_encodings=feature_encodings,
+        # assign each sample to a custom group label
+        group_labels=["Custom Group " + str(i % 4) for i in range(len(X))],
     )
     run_id = mlflow.active_run().info.run_id
     print(f"Run ID: {run_id}. If you are running mlflow locally use:\npython -m mlflow ui --port 5000\nThen open http://localhost:5000/#/experiments/{mlflow.get_experiment_by_name(experiment_name).experiment_id}/runs/{run_id} to view the report.",

src/xaiflow/mlflow_plugin.py

@@ -26,7 +26,7 @@ def __init__(self):
         self.template_dir = os.path.join(os.path.dirname(__file__), 'templates')
         self.env = Environment(loader=FileSystemLoader(self.template_dir))
 
-    def log_feature_importance_report(
+    def log_xai_report(
         self,
         feature_names: List[str],
         shap_values: Explanation,

src/xflow.egg-info/PKG-INFO

@@ -80,7 +80,7 @@ with mlflow.start_run():
 
     # Add interactive explainable AI reports
     plugin = CEMLflowPlugin()
-    plugin.log_feature_importance_report(
+    plugin.log_xai_report(
         feature_names=X.columns.tolist(),
         shap_values=shap_values,
         report_name="model_explanation.html"
@@ -121,7 +121,7 @@ xaiflow/
 ### Core Components
 
 **MLflow Integration** (`mlflow_plugin.py`)
-The `CEMLflowPlugin` class handles the integration with MLflow. The main method `log_feature_importance_report()` processes SHAP values, manages feature encodings, and stores the generated reports as MLflow artifacts.
+The `CEMLflowPlugin` class handles the integration with MLflow. The main method `log_xai_report()` processes SHAP values, manages feature encodings, and stores the generated reports as MLflow artifacts.
 
 **Report Generation** (`report_generator.py`)
 The `ReportGenerator` class converts SHAP data into interactive HTML reports using Jinja2 templating. It handles template loading, asset bundling, and data injection into the frontend components.
@@ -167,7 +167,7 @@ feature_encodings = {
     'region': {0: 'North', 1: 'South', 2: 'East', 3: 'West'}
 }
 
-plugin.log_feature_importance_report(
+plugin.log_xai_report(
     feature_names=feature_names,
     shap_values=shap_values,
     feature_encodings=feature_encodings,

tests/test_mlflow_plugin.py

@@ -4,8 +4,6 @@
 import numpy as np
 from sklearn.datasets import fetch_openml
 from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
-from xgboost import XGBClassifier
-from catboost import CatBoostClassifier
 from sklearn.preprocessing import LabelEncoder
 import shap
 from typing import Callable
@@ -187,7 +185,7 @@ def __exit__(self, exc_type, exc_val, exc_tb):
     mocker.patch("mlflow.log_artifact")
 
     with mlflow.start_run(run_name="auto_mpg_test"):
-        plugin.log_feature_importance_report(
+        plugin.log_xai_report(
             shap_values=shap_values,
             feature_encodings=feature_encodings,
             feature_names=list(X.columns),
@@ -263,7 +261,7 @@ def __exit__(self, exc_type, exc_val, exc_tb):
     mocker.patch("mlflow.log_artifact")
 
     with mlflow.start_run(run_name="auto_mpg_test"):
-        plugin.log_feature_importance_report(
+        plugin.log_xai_report(
             shap_values=shap_values,
             feature_encodings=feature_encodings,
             feature_names=list(X.columns),
@@ -319,11 +317,10 @@ def __exit__(self, exc_type, exc_val, exc_tb):
     mocker.patch("mlflow.log_artifact")
 
     with mlflow.start_run(run_name="auto_mpg_test"):
-        plugin.log_feature_importance_report(
+        plugin.log_xai_report(
             shap_values=shap_values,
             feature_encodings=feature_encodings,
             feature_names=list(X.columns),
             group_labels=["Group 1", "Group 2", "Group 3", "Group 4"] * int(len(shap_values) / 4)  # Example group labels
         )
-        html_content_click_test(Path(output_path))
-    # return html_content
+        html_content_click_test(Path(output_path))