Tutorial¶

This Python client communicates with the Avatar platform.

For more information about the Avatar method and process, check out our main docs at https://docs.octopize.io

Step 1: Setup and Authentication¶

First, import the necessary libraries and set up your authentication.

Option A: API Key Authentication (Recommended)¶

import os
from avatars.manager import Manager

# Initialize the Manager with an API key
manager = Manager(api_key=os.environ.get("AVATAR_API_KEY"))

Option B: Username/Password Authentication¶

import os
from avatars.manager import Manager

# Set up the URL and your credentials
username = os.environ.get("AVATAR_USERNAME")
password = os.environ.get("AVATAR_PASSWORD")

# Initialize the Manager and authenticate
manager = Manager()  # or manager = Manager(base_url="https://your-server.com")
manager.authenticate(username, password)

Make sure to configure your credentials in your environment variables.

Step 2: Uploading Your Data¶

Once authenticated, you can upload your data table to the server. In this example, we’ll use a CSV file wbcd.csv.

# Initialize the runner
runner = manager.create_runner("test_wbcd")

# Add the table to the runner
runner.add_table("wbcd", "fixtures/wbcd.csv")

Here, the wbcd dataset is uploaded and is now ready for anonymization.

Step 3: Avatarization Parameters and Execution¶

Next, set the avatarization parameters and run the avatarization process:

# Set anonymization parameters
runner.set_parameters("wbcd", k=10)  # Adjust k value for privacy level

# Run the anonymization pipeline
runner.run()

# Get all results after running the anonymization
results = runner.get_all_results()

Here, k=10 is the number of nearest neighbors used in the KNN-based anonymization). You can adjust this value based on your desired privacy level. If you want to know more about anonymization parameters, please refer to the user guide

Step 4: Retrieve Avatarized Data¶

Once the anonymization is complete, you can retrieve and inspect the avatarized data:

# Print a preview of the avatarized data
print("Avatar data:")
print(runner.shuffled("wbcd").head())

Step 5: Retrieve Privacy and Signal Metrics¶

Octopize provides privacy and signal metrics to evaluate the quality of the synthetic data produced:

# Print privacy metrics
for key, value in runner.privacy_metrics("wbcd")[0].items():
    print(f"{key}: {value}")

# Print signal metrics
for key, value in runner.signal_metrics("wbcd")[0].items():
    print(f"{key}: {value}")

Step 6: Download Avatarization Report¶

Once you found the optimal set of parameters for your use case, you can download a comprehensive report of the anonymization process. This report compiles the privacy and utility metrics obtained, providing evidence of both anonymity and the preservation of statistical properties.

# Download the anonymization report as a PDF
runner.download_report('my_report.pdf')

Once you’ve followed these steps, you’re ready to explore further and fine-tune your anonymization process!

Further Resources¶

You can explore more features of the Avatar solution by following our notebook tutorials.