sdqcpy 1.0.1

SDQCPy is a comprehensive Python package designed for synthetic data management, quality control, and validation.

SDQCPy

SDQCPy: A Comprehensive Python Package for Synthetic Data Management

中文版本

Table of Contents

• Features
• Installation
• Results Display
• Usage
  • Demo
  • Data Synthesis
• Workflow
• Support
• License

Features

SDQCPy offers a comprehensive toolkit for synthetic data generation, quality assessment, and analysis:

1. Data Synthesis: Generate synthetic data using various models.
2. Quality Evaluation: Assess synthetic data quality through statistical tests, classification metrics, explainability analysis, and causal inference.
3. End-to-End Analysis: Perform holistic analysis by integrating multiple evaluation methods to provide a comprehensive view of synthetic data quality.
4. Results Display: Store the results in a HTML file.

Installation

You can install SDQCPy using pip:

pip install sdqcpy

Alternatively, you can install it from the source:

git clone https://github.com/T0217/sdqcpy.git
cd sdqcpy
pip install -e .

Results Display

SDQCPy provides a SequentialAnalysis class to perform the sequential analysis and store the results in a HTML file.

Usage

Demo

You can use the following code to achieve the sequential analysis and store the results in a HTML file:

from sdqc_integration import SequentialAnalysis
from sdqc_data import read_data
import logging
import warnings

# Ignore warnings and set logging level to ERROR
warnings.filterwarnings('ignore')
logger = logging.getLogger()
logger.setLevel(logging.ERROR)

# Set random seed
random_seed = 17

# Replace with your own data path and use pandas to read the data
raw_data = read_data('3_raw')
synthetic_data = read_data('3_synth')

output_path = 'raw_synth.html'

# Perform sequential analysis
sequential = SequentialAnalysis(
    raw_data=raw_data,
    synthetic_data=synthetic_data,
    random_seed=random_seed,
    use_cols=None,
)
results = sequential.run()
sequential.visualize_html(output_path)

Data Synthesis

SDQCPy supports various methods, the implementation of these methods are using ydata-synthetic and SDV.

[!TIP]

We only display simple code here, and the parameters of each model can be further modified as needed.

• YData Synthesizer

  import pandas as pd
  from sdqc_synthesize import YDataSynthesizer
  
  raw_data = pd.read_csv("raw_data.csv")  # Please replace with your own data path
  ydata_synth = YDataSynthesizer(data=raw_data)
  synthetic_data = ydata_synth.generate()
  

[!IMPORTANT]

In the latest version, ydata-synthetic has switched to using ydata-sdk. However, since synthetic data is only a supplementary feature of this library, it has not been updated yet.

• SDV Synthesizer

  import pandas as pd
  from sdqc_synthesize import SDVSynthesizer
  
  raw_data = pd.read_csv("raw_data.csv")  # Please replace with your own data path
  sdv_synth = SDVSynthesizer(data=raw_data)
  synthetic_data = sdv_synth.generate()
  

Workflow

SDQCPy use the process shown below to perform the quality check and analysis:

---
title Main Idea
---
flowchart TB
	%% Define the style
	classDef default stroke:#000,fill:none

	%% Define the nodes
	initial([Input Real Data and Synthetic Data])
	step1[Statistical Test]
	step2[Classification]
	step3[Explainability]
	step4[Causal Analysis]
	endprocess[Export HTML file]

    %% Define the relationships between nodes
    initial --> step1
    step1 --> step2
    step2 --> step3
    step3 --> step4
    step4 --> endprocess

• Statistical Test SDQCPy employs various methods for descriptive analysis, distribution comparison, and correlation testing tailored to different data types.
• Classification SDQCPy employs machine learning models(SVC, RandomForestClassifier, XGBClassifier, LGBMClassifier) to evaluate the similarity between the real and synthetic data.
• Explainability SDQCPy employs several of the current mainstream explainability methods(Model-Based,SHAP, PFI) to evaluate the explainability of the synthetic data.
• Causal Analysis SDQCPy employs several causal structure learning methods and evaluation metrics to compare the adjacency matrix of the raw and synthetic data. The implementation of these methods are using gCastle.
• End-to-End Analysis(named SequentialAnalysis) To streamline the process of calling individual modules one by one, we have integrated all the functions. If you have specific needs, you can also use these functions along your lines.

Support

Need help? Report a bug? Ideas for collaborations? Reach out via GitHub Issues

[!IMPORTANT]

Before reporting an issue on GitHub, please check the existing Issues to avoid duplicates.

If you wish to contribute to this library, please first open an Issue to discuss your proposed changes. Once discussed, you are welcome to submit a Pull Request.

License

Apache-2.0 @T0217