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 are images that visually describe model performance metrics. For example, confusion matrices, ROC curves, model bias tests, and training loss curves are examples of common model evaluation images. By attaching model evaluation images to your versioned model, other users can better understand the model performance and evaluation metrics.

Code#

Step 1: Connect to Hopsworks#

import hopsworks

project = hopsworks.login()

# get Hopsworks Model Registry handle
mr = project.get_model_registry()

Step 2: Generate model evaluation images#

Generate an image that visualizes model performance and evaluation metrics

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 figure to a file inside the model directory#

Save the figure to a file with a common filename extension (for example, .png or .jpeg), and place it in a directory called images - a subdirectory of the model directory that is registered to Hopsworks.

# Specify the directory name for saving the model and related artifacts
model_dir = "./model"

# Create a subdirectory of model_dir called '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, visually communicating the model performance and evaluation metrics in the model registry.