augini 0.2.0

A framework for generating synthetic tabular data using AI

Augini

Augini is a Python framework for generating synthetic tabular data using AI. It leverages the power of language models to create realistic, fictional data based on existing datasets.

Installation

You can install Augini using pip:

pip install augini

Quick Start

Here's a simple example of how to use Augini:

from augini import Augini
import pandas as pd

# Initialize Augini
augini = Augini(api_key="your_api_key", use_openrouter=True)

# Create a sample DataFrame
data = {
    'Place of Birth': ['New York', 'London', 'Tokyo'],
    'Age': [30, 25, 40],
    'Gender': ['Male', 'Female', 'Male']
}
df = pd.DataFrame(data)

# Add synthetic features
result_df = augini.augment_columns(df, 'NAME', 'OCCUPATION', 'FAVORITE_DRINK')

print(result_df)

Features

• Generate synthetic data based on existing datasets
• Customizable prompts for data generation
• Support for both OpenAI API and OpenRouter
• Asynchronous processing for improved performance

Extending and Enriching Data

Augini can be used to extend, augment, and enrich your datasets by adding synthetic features and bringing knowledge from language models to your data.

Adding Multiple Features

You can add multiple features to your DataFrame:

result_df = augini.augment_columns(df, 'Hobby', 'FavoriteColor', 'FavoriteMovie')
print(result_df)

Custom Prompts for Feature Generation

Custom prompts allow you to generate specific features based on your needs:

custom_prompt = "Based on the person's name and age, suggest a quirky pet for them. Respond with a JSON object with the key 'QuirkyPet'."
result_df = augini.augment_single(df, 'QuirkyPet', custom_prompt=custom_prompt)
print(result_df)

Anonymizing Data

You can anonymize sensitive information in your dataset by generating synthetic data:

anonymize_prompt = "Create an anonymous profile for the person based on their age and city. Respond with a JSON object with keys 'AnonymousName' and 'AnonymousEmail'."
result_df = augini.augment_single(df, 'AnonymousProfile', custom_prompt=anonymize_prompt)
print(result_df)

Bringing Knowledge from LLMs

Leverage the knowledge embedded in language models to enhance your datasets:

Generating Detailed Descriptions

description_prompt = "Generate a detailed description for a person based on their age and city. Respond with a JSON object with the key 'Description'."
result_df = augini.augment_single(df, 'Description', custom_prompt=description_prompt)
print(result_df)

Suggesting Recommendations

recommendation_prompt = "Suggest a book and a movie for a person based on their age and city. Respond with a JSON object with keys 'RecommendedBook' and 'RecommendedMovie'."
result_df = augini.augment_single(df, 'Recommendations', custom_prompt=recommendation_prompt)
print(result_df)

Full Example

Here's a full example demonstrating multiple features and custom prompts:

from augini import Augini
import pandas as pd

# Initialize Augini
augini = Augini(api_key="your_api_key", use_openrouter=True)

# Create a sample DataFrame
data = {
    'Name': ['Alice Johnson', 'Bob Smith', 'Charlie Davis'],
    'Age': [28, 34, 45],
    'City': ['New York', 'Los Angeles', 'Chicago']
}
df = pd.DataFrame(data)

# Add multiple synthetic features
result_df = augini.augment_columns(df, 'Occupation', 'Hobby', 'FavoriteColor')

# Add a custom feature
custom_prompt = "Based on the person's name and age, suggest a quirky pet for them. Respond with a JSON object with the key 'QuirkyPet'."
result_df = augini.augment_single(result_df, 'QuirkyPet', custom_prompt=custom_prompt)

# Anonymize data
# Initialize Augini with your API key

from augini import Augini
import pandas as pd

api_key = "OpenAI or OpenRouter"


augini = Augini(api_key=api_key, use_openrouter=True, model='meta-llama/llama-3-8b-instruct')

# Create a sample DataFrame with sensitive information
data = {
    'Name': ['Alice Johnson', 'Bob Smith', 'Charlie Davis'],
    'Age': [28, 34, 45],
    'City': ['New York', 'Los Angeles', 'Chicago'],
    'Email': ['[e-mail]', '[e-mail]', '[e-mail]'],
    'Phone': ['123-456-7890', '987-654-3210', '555-555-5555']
}
df = pd.DataFrame(data)

# Define a general anonymization prompt
anonymize_prompt = (
    "Given the information from the dataset, create an anonymized version that protects individual privacy while maintaining data utility. "
    "Follow these guidelines:\n\n"
    "1. K-Anonymity: Ensure that each combination of quasi-identifiers (e.g., age, city) appears at least k times in the dataset. "
    "Use generalization or suppression techniques as needed.\n"
    "2. L-Diversity: For sensitive attributes, ensure there are at least l well-represented values within each equivalence class.\n"
    "3. Direct Identifiers: Replace the following with synthetic data:\n"
    "   - Names: Generate culturally appropriate fictional names\n"
    "   - Email addresses: Create plausible fictional email addresses\n"
    "   - Phone numbers: Generate realistic but non-functional phone numbers\n"
    "4. Quasi-Identifiers: Apply generalization or suppression as needed:\n"
    "   - Age: Consider using age ranges instead of exact ages\n"
    "   - City: Use broader geographic regions if necessary\n"
    "5. Sensitive Attributes: Maintain the statistical distribution of sensitive data while ensuring diversity.\n"
    "6. Data Consistency: Ensure that the anonymized data remains internally consistent and plausible.\n"
    "7. Non-Sensitive Data: Keep unchanged unless required for k-anonymity or l-diversity.\n\n"
    "Respond with a JSON object containing the anonymized values for all fields. "
    "Ensure the anonymized dataset maintains utility for analysis while protecting individual privacy."
)

# Use the augment_columns method to anonymize the data
result_df = augini.augment_columns(df, ['Name_A', 'Email_A', 'Age_A', 'City_A'], custom_prompt=anonymize_prompt)

# Display the resulting DataFrame
print(result_df)

Contributing

We welcome contributions to enhance Augini! Feel free to open issues and submit pull requests on our GitHub repository.