From 64b884df98aeb4a4d8d5f57b17d95fea6a3034d6 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Javier=20de=20la=20R=C3=BAa=20Mart=C3=ADnez?=
 <javier@logicalclocks.com>
Date: Mon, 8 Apr 2024 11:06:45 +0200
Subject: [PATCH] [HWORKS-1113] Add docs for attaching model evaluation images
 to a model version

---
 .../mlops/registry/model_evaluation_images.md | 75 +++++++++++++++++++
 mkdocs.yml                                    |  1 +
 2 files changed, 76 insertions(+)
 create mode 100644 docs/user_guides/mlops/registry/model_evaluation_images.md

diff --git a/docs/user_guides/mlops/registry/model_evaluation_images.md b/docs/user_guides/mlops/registry/model_evaluation_images.md
new file mode 100644
index 000000000..9406917ec
--- /dev/null
+++ b/docs/user_guides/mlops/registry/model_evaluation_images.md
@@ -0,0 +1,75 @@
+# How To Save Model Evaluation Images
+
+## Introduction
+
+In this guide, you will learn how to attach ==model evaluation images== to a model. Model evaluation images contain **confusion matrices**, **ROC curves** or other graphs that help visualizing model evaluation metrics. By attaching model evaluation images to your model version, other users can better understand the experiment results obtained during model training.
+
+## Code
+
+### Step 1: Connect to Hopsworks
+
+```python
+import hopsworks
+
+project = hopsworks.login()
+
+# get Hopsworks Model Registry handle
+mr = project.get_model_registry()
+```
+
+### Step 2: Generate model evaluation figures
+
+Generate a figure that visualizes a model metric. 
+
+```python
+import seaborn
+from sklearn.metrics import confusion_matrix
+
+# Predict the training data using the trained model
+y_pred_train = model.predict(X_train)
+
+# Predict the test data using the trained model
+y_pred_test = model.predict(X_test)
+
+# Calculate and print the confusion matrix for the test predictions
+results = confusion_matrix(y_test, y_pred_test)
+
+# Create a DataFrame for the confusion matrix results
+df_confusion_matrix = pd.DataFrame(
+    results, 
+    ['True Normal', 'True Fraud'],
+    ['Pred Normal', 'Pred Fraud'],
+)
+
+# Create a heatmap using seaborn with annotations
+heatmap = seaborn.heatmap(df_confusion_matrix, annot=True)
+
+# Get the figure and display it
+fig = heatmap.get_figure()
+fig.show()
+```
+
+### Step 3: Save the figures as images inside the model directory
+
+Save the figure to an image file, and place it in a directory with name ´images´ inside the model directory to be exported.
+
+```python
+# Specify the directory name for saving the model and related artifacts
+model_dir = "./model"
+
+# Create a directory with name 'images' for saving the model evaluation images
+model_images_dir = model_dir + "/images"
+if not os.path.exists(model_images_dir):
+    os.mkdir(model_images_dir)
+
+# Save the figure to an image file in the images directory
+fig.savefig(model_images_dir + "/confusion_matrix.png")
+
+# Register the model
+py_model = mr.python.create_model(name="py_model")
+py_model.save("./model")
+```
+
+## Conclusion
+
+In this guide you learned how to attach model evaluation images to a model, helping better understand the experiment results obtained during model training.
\ No newline at end of file
diff --git a/mkdocs.yml b/mkdocs.yml
index 9b7d1e657..7039d8a83 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -183,6 +183,7 @@ nav:
                   - Python: user_guides/mlops/registry/frameworks/python.md
               - Model Schema: user_guides/mlops/registry/model_schema.md
               - Input Example: user_guides/mlops/registry/input_example.md
+              - Model Evaluation Images: user_guides/mlops/registry/model_evaluation_images.md
           - Model Serving:
               - user_guides/mlops/serving/index.md
               - Deployment: