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    Synthetic Data Generation -
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    Installation

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    ydata-synthetic is available through PyPi, allowing an easy process of installation and integration with the data science programing environments (Google Colab, Jupyter Notebooks, Visual Studio Code, PyCharm) and stack (pandas, numpy, scikit-learn).

    +

    ydata-sdk is available through PyPi, allowing an easy process of installation and integration with the data science programing environments (Google Colab, Jupyter Notebooks, Visual Studio Code, PyCharm) and stack (pandas, numpy, scikit-learn).

    Installing the package

    -

    Currently, the package supports python versions over 3.9, and can be installed in Windows, Linux or MacOS operating systems.

    +

    Currently, the package supports python versions over 3.9 and up-to python 3.12, and can be installed in Windows, Linux or MacOS operating systems.

    Prior to the package installation, it is recommended the creation of a virtual or conda environment:

    -
    conda create -n synth-env python=3.10
+
    conda create -n synth-env python=3.12
 conda activate synth-env
    -

    The above command creates and activates a new environment called "synth-env" with Python version 3.10.X. In the new environment, you can then install ydata-synthetic:

    +

    The above command creates and activates a new environment called "synth-env" with Python version 3.12.X. In the new environment, you can then install ydata-sdk:

    -
    pip install ydata-synthetic==1.1.0
+
    pip install ydata-sdk
    @@ -1560,15 +947,9 @@

    Installing the package

    5min – Step-by-step installation guide

    Using Google Colab

    To install inside a Google Colab notebook, you can use the following:

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    !pip install ydata-synthetic==1.1.0
-
    -

    Make sure your Google Colab is running Python versions >=3.9, <3.11. Learn how to configure Python versions on Google Colab here.

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    Installing the Streamlit App

    -

    Since version 1.0.0, the ydata-synthetic includes a GUI experience provided by a Streamlit app. The UI supports the data synthesization process from reading the data to profiling the synthetic data generation, and can be installed as follows:

    -
    pip install "ydata-synthetic[streamlit]"
+
    !pip install ydata-sdk
 
    
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    Note that Jupyter or Colab Notebooks are not yet supported, so use it in your Python environment.
    
-
    
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    Make sure your Google Colab is running Python versions >=3.9, <=3.12. Learn how to configure Python versions on Google Colab here.
    
 
 
 
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    Quickstart
    
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    ydata-synthetic is equipped to handle both tabular (comprising numeric and categorical features) and sequential, time-series data. In this section we explain how you can quickstart the synthesization of tabular and time-series datasets.
    
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    Synthesizing a Tabular Dataset
    
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    The following example showcases how to synthesize the Adult Census Income dataset with CTGAN:
    
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        # Import the necessary modules
-    from pmlb import fetch_data
-    from ydata_synthetic.synthesizers.regular import RegularSynthesizer
-    from ydata_synthetic.synthesizers import ModelParameters, TrainParameters
-
-    # Load data
-    data = fetch_data('adult')
-    num_cols = ['age', 'fnlwgt', 'capital-gain', 'capital-loss', 'hours-per-week']
-    cat_cols = ['workclass','education', 'education-num', 'marital-status',
-                'occupation', 'relationship', 'race', 'sex', 'native-country', 'target']
-
-    # Define model and training parameters
-    ctgan_args = ModelParameters(batch_size=500, lr=2e-4, betas=(0.5, 0.9))
-    train_args = TrainParameters(epochs=501)
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-    # Train the generator model
-    synth = RegularSynthesizer(modelname='ctgan', model_parameters=ctgan_args)
-    synth.fit(data=data, train_arguments=train_args, num_cols=num_cols, cat_cols=cat_cols)
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-    synth_data = synth.sample(1000) 
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    The following example showcases how to synthesize the Yahoo Stock Price dataset with TimeGAN:
    
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        # Import the necessary modules
-    import pandas as pd
-    from ydata_synthetic.synthesizers.timeseries import TimeSeriesSynthesizer
-    from ydata_synthetic.synthesizers import ModelParameters, TrainParameters
-
-    # Define model parameters
-    gan_args = ModelParameters(batch_size=128,
-                            lr=5e-4,
-                            noise_dim=32,
-                            layers_dim=128,
-                            latent_dim=24,
-                            gamma=1)
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-    train_args = TrainParameters(epochs=50000,
-                                sequence_length=24,
-                                number_sequences=6)
-
-    # Read the data
-    stock_data = pd.read_csv("stock_data.csv")
-
-    # Training the TimeGAN synthesizer
-    synth = TimeSeriesSynthesizer(modelname='timegan', model_parameters=gan_args)
-    synth.fit(stock_data, train_args, num_cols=list(stock_data.columns))
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-    # Generating new synthetic samples
-    synth_data = synth.sample(n_samples=500)
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    Running the Streamlit App
    
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    Once the package is installed with the "streamlit" extra, the app can be launched as:
    
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     Here's a quick example of how to synthesize data with the Streamlit App  –  5min
    
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  • - - Current Functionality + + Supported Data Types
  • - - Supported Data Types + + Validate the quality of your synthetic data generated
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    Overview

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    Overview

    GitHub stars Discord

    Overview

    -

    ydata-synthetic is the go-to Python package for synthetic data generation for tabular and time-series data. It uses the latest Generative AI models to learn the properties of real data and create realistic synthetic data. This project was created to educate the community about synthetic data and its applications in real-world domains, such as data augmentation, bias mitigation, data sharing, and privacy engineering. To learn more about Synthetic Data and its applications, check this article.

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    Current Functionality

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      🤖 Create Realistic Synthetic Data using Generative AI Models: ydata-synthetic supports the state-of-the-art generative adversarial networks for data generation, namely Vanilla GAN, CGAN, WGAN, WGAN-GP, DRAGAN, Cramer GAN, CWGAN-GP, CTGAN, and TimeGAN. Learn more about the use of GANs for Synthetic Data generation.

      -
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    • -

      📀 Synthetic Data Generation for Tabular and Time-Series Data: The package supports the synthesization of tabular and time-series data, covering a wide range of real-world applications. Learn how to leverage ydata-synthetic for tabular and time-series data.

      -
      • -
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      💻 Best Generation Experience in Open Source: Including a guided UI experience for the generation of synthetic data, from reading the data to visualization of synthetic data. All served by a slick Streamlit app. - Here's a quick overview – 1min

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    Question

    -

    Looking for an end-to-end solution to Synthetic Data Generation?

    -

    YData Fabric enables the generation of high-quality datasets within a full UI experience, from data preparation to synthetic data generation and evaluation. Check out the Community Version.

    +

    YData-Synthetic is an open-source package developed in 2020 with the primary goal of educating users about generative models for synthetic data generation. +Designed as a collection of models, it was intended for exploratory studies and educational purposes. +However, it was not optimized for the quality, performance, and scalability needs typically required by organizations.

    +
    +

    We are now ydata-sdk!

    +

    Even though the journey was fun, and we have learned a lot from the community it is now time to upgrade ydata-synthetic.

    +

    Heading towards the future of synthetic data generation we recommend users to transition to ydata-sdk, which provides a superior experience with enhanced performance, +precision, and ease of use, making it the preferred tool for synthetic data generation and a perfect introduction to Generative AI.

    Supported Data Types

    -
    +

    Tabular data does not have a temporal dependence, and can be structured and organized in a table-like format, where features are represented in columns, whereas observations correspond to the rows.

    Additionally, tabular data usually comprises both numeric and categorical features. Numeric features are those that encode quantitative values, whereas categorical represent qualitative measurements. Categorical features can further divided in ordinal, binary or boolean, and nominal features.

    -

    Learn more about synthesizing tabular data in this article, or check the quickstart guide to get started with the synthesization of tabular datasets.

    +

    Learn more about synthesizing tabular data in this article, or check the quickstart guide to get started with the synthesization of tabular datasets.

    Time-series data exhibit a sequencial, temporal dependency between records, and may present a wide range of patterns and trends, including seasonality (patterns that repeat at calendar periods -- days, weeks, months -- such as holiday sales, for instance) or periodicity (patterns that repeat over time).

    -

    Read more about generating time-series data in this article and check this quickstart guide to get started with time-series data synthesization.

    +

    Read more about generating time-series data in this article and check this quickstart guide to get started with time-series data synthesization.

    +
    +

    Multi-Table data or databases exhibit a referential behaviour between and database schema that is expected to be replicated and respected by the synthetic data generated. +Read more about database synthetic data generation in this article and check this quickstart guide for Multi-Table synthetic data generation +Time-series data exhibit a sequential, temporal dependency between records, and may present a wide range of patterns and trends, including seasonality (patterns that repeat at calendar periods -- days, weeks, months -- such as holiday sales, for instance) or periodicity (patterns that repeat over time).

    +
    +

    Validate the quality of your synthetic data generated

    +

    Validating the quality of synthetic data is essential to ensure its usefulness and privacy. YData Fabric provides tools for comprehensive synthetic data evaluation through:

    +
      +
    1. +

      Profile Comparison Visualization: +Fabric delivers side-by-side visual comparisons of key data properties (e.g., distributions, correlations, and outliers) between synthetic and original datasets, allowing users to assess fidelity at a glance.

      +
      2. +
    2. +

      PDF Report with Metrics: +Fabric generates a PDF report that includes key metrics to evaluate:

      +
      3. +
    3. +

      Fidelity: How closely synthetic data matches the original.

      +
      4. +
    4. Utility: How well it performs in real-world tasks.
      5. +
    5. Privacy: Risk assessment of data leakage and re-identification.
      6. +
    +

    These tools ensure a thorough validation of synthetic data quality, making it reliable for real-world use.

    Supported Generative AI Models

    -

    The following architectures are currently supported:

    +

    With the upcoming update of ydata-syntheticto ydata-sdk, users will now have access to a single API that automatically selects and optimizes +the best generative model for their data. This streamlined approach eliminates the need to choose between +various models manually, as the API intelligently identifies the optimal model based on the specific dataset and use case.

    +

    Instead of having to manually select from models such as:

    • GAN
    • CGAN (Conditional GAN)
      • @@ -1601,6 +1011,9 @@

      Supported Generative AI Models

    • TimeGAN (specifically for time-series data)
    • DoppelGANger (specifically for time-series data)
    +

    The new API handles model selection automatically, optimizing for the best performance in fidelity, utility, and privacy. +This significantly simplifies the synthetic data generation process, ensuring that users get the highest quality output without +the need for manual intervention and tiring hyperparameter tuning.

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    For a use-case oriented UI experience, try YData Fabric. From an interactive and complete data profiling to an efficient synthetization, your data preparation process will be seamlessly adjusted to your data characteristics.

    -

    How can I run the Streamlit app?

    -

    To try ydata-synthetic using the streamlit app, you need to install it using the [] notation that encodes the extras that the package incorporates. In this case, you can simply create your virtual environment and install ydata-synthetic as:

    -
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    Note that Jupyter or Colab Notebooks are not yet supported, so you need to work it out in your Python environment. Once the package is installed, you can use the following snippet to start the app:

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    What is the best way to evaluate the quality of my synthetic data?

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      Most issues with installations are usually associated with unsupported Python versions or misalignment between python environments and package requirements.

      Let’s see how you can get both right:

      Python Versions

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      Note that ydata-synthetic currently requires Python >=3.9, < 3.11 so if you're trying to run our code in Google Colab, then you need to update your Google Colab’s Python version accordingly. The same goes for your development environment.

      +

      Note that ydata-sdk currently requires Python >=3.9, < 3.13 so if you're trying to run our code in Google Colab, then you need to update your Google Colab’s Python version accordingly. The same goes for your development environment.

      Virtual Environments

      A lot of troubleshooting arises due to misalignments between environments and package requirements. Virtual Environments isolate your installations from the "global" environment so that you don't have to worry about conflicts.

      Using conda, creating a new environment is as easy as running this on your shell:

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      conda create --name synth-env python==3.9 pip
-conda activate synth-env
-pip install ydata-synthetic
+
      conda create --name synth-env python==3.12 pip
+conda activate synth-env
+pip install ydata-sdk
 
      
 
      Now you can open up your Python editor or Jupyter Lab and use the synth-env as your development environment, without having to worry about conflicting versions or packages between projects!
      
 
      Does TimeGAN replicate my full sequence of data?
      
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    Using CRAMER GAN to generate tabular synthetic data:

    Real-world domains are often described by tabular data i.e., data that can be structured and organized in a table-like format, where features/variables are represented in columns, whereas observations correspond to the rows.

    CRAMER GAN is a variant of GAN that employs the Cramer distance as a measure of similarity between real and generated data distributions to improve training stability and enhance sample quality:

    diff --git a/1.4/synthetic_data/single_table/ctgan_example/index.html b/1.4/synthetic_data/single_table/ctgan_example/index.html index 94fd2c78..0d2e8cfb 100644 --- a/1.4/synthetic_data/single_table/ctgan_example/index.html +++ b/1.4/synthetic_data/single_table/ctgan_example/index.html @@ -195,22 +195,6 @@ - - - - - -
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    Using CTGAN to generate tabular synthetic data:

    Real-world domains are often described by tabular data i.e., data that can be structured and organized in a table-like format, where features/variables are represented in columns, whereas observations correspond to the rows.

    Additionally, real-world data usually comprises both numeric and categorical features. Numeric features are those that encode quantitative values, whereas categorical represent qualitative measurements.

    diff --git a/1.4/synthetic_data/single_table/cwgangp_example/index.html b/1.4/synthetic_data/single_table/cwgangp_example/index.html index 61d5200a..5f23d184 100644 --- a/1.4/synthetic_data/single_table/cwgangp_example/index.html +++ b/1.4/synthetic_data/single_table/cwgangp_example/index.html @@ -195,22 +195,6 @@ - - - - - -
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    Using CWGAN-GP to generate tabular synthetic data:

    Real-world domains are often described by tabular data i.e., data that can be structured and organized in a table-like format, where features/variables are represented in columns, whereas observations correspond to the rows.

    CWGAN GP is a variant of GAN that incorporates conditional information to generate data samples, while leveraging the Wasserstein distance to improve training stability and sample quality:

    diff --git a/1.4/synthetic_data/single_table/dragan_example/index.html b/1.4/synthetic_data/single_table/dragan_example/index.html index bf82261a..e37d4791 100644 --- a/1.4/synthetic_data/single_table/dragan_example/index.html +++ b/1.4/synthetic_data/single_table/dragan_example/index.html @@ -195,22 +195,6 @@ - - - - - -
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    Using DRAGAN to generate tabular synthetic data:

    Real-world domains are often described by tabular data i.e., data that can be structured and organized in a table-like format, where features/variables are represented in columns, whereas observations correspond to the rows.

    DRAGAN is a GAN variant that uses a gradient penalty to improve training stability and mitigate mode collapse:

    diff --git a/1.4/synthetic_data/single_table/gmm_example/index.html b/1.4/synthetic_data/single_table/gmm_example/index.html index 242b8e33..c4d467a4 100644 --- a/1.4/synthetic_data/single_table/gmm_example/index.html +++ b/1.4/synthetic_data/single_table/gmm_example/index.html @@ -9,7 +9,7 @@ - + @@ -195,22 +195,6 @@ - - - - - -
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    Using GMMs to generate tabular synthetic data:

    Real-world domains are often described by tabular data i.e., data that can be structured and organized in a table-like format, where features/variables are represented in columns, whereas observations correspond to the rows.

    diff --git a/1.4/synthetic_data/single_table/wgan_example/index.html b/1.4/synthetic_data/single_table/wgan_example/index.html index 6a914fd8..a9335160 100644 --- a/1.4/synthetic_data/single_table/wgan_example/index.html +++ b/1.4/synthetic_data/single_table/wgan_example/index.html @@ -195,22 +195,6 @@ - - - - - -
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    Using WGAN to generate tabular synthetic data:

    Real-world domains are often described by tabular data i.e., data that can be structured and organized in a table-like format, where features/variables are represented in columns, whereas observations correspond to the rows.

    WGAN is a variant of GAN that utilizes the Wasserstein distance to improve training stability and generate higher quality samples:

    diff --git a/1.4/synthetic_data/single_table/wgangp_example/index.html b/1.4/synthetic_data/single_table/wgangp_example/index.html index 27988e13..ef77f48b 100644 --- a/1.4/synthetic_data/single_table/wgangp_example/index.html +++ b/1.4/synthetic_data/single_table/wgangp_example/index.html @@ -195,22 +195,6 @@ - - - - - -
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    Using WGAN-GP to generate tabular synthetic data:

    Real-world domains are often described by tabular data i.e., data that can be structured and organized in a table-like format, where features/variables are represented in columns, whereas observations correspond to the rows.

    WGANGP is a variant of GAN that incorporates a gradient penalty term to enhance training stability and improve the diversity of generated samples:

    diff --git a/1.4/synthetic_data/streamlit_app/index.html b/1.4/synthetic_data/streamlit_app/index.html deleted file mode 100644 index 1371210c..00000000 --- a/1.4/synthetic_data/streamlit_app/index.html +++ /dev/null @@ -1,1623 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - UI interface - Streamlit app - YData-Synthetic - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    ´ydata-synthetic´ offers a UI interface to guide you through the steps and inputs to generate structure tabular data. -The streamlit app is available from v1.0.0 onwards, and supports the following flows:

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    I know you probably love Jupyter Notebooks or Google Colab, but make sure that you start your -synthetic data generation streamlit app from a python script as notebooks are not supported!

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    Using DoppelGANger to generate synthetic time-series data:

    Although tabular data may be the most frequently discussed type of data, a great number of real-world domains — from traffic and daily trajectories to stock prices and energy consumption patterns — produce time-series data which introduces several aspects of complexity to synthetic data generation.

    Time-series data is structured sequentially, with observations ordered chronologically based on their associated timestamps or time intervals. It explicitly incorporates the temporal aspect, allowing for the analysis of trends, seasonality, and other dependencies over time.

    diff --git a/1.4/synthetic_data/time_series/timegan_example/index.html b/1.4/synthetic_data/time_series/timegan_example/index.html index 1dba6e51..c2ff6cbf 100644 --- a/1.4/synthetic_data/time_series/timegan_example/index.html +++ b/1.4/synthetic_data/time_series/timegan_example/index.html @@ -195,22 +195,6 @@ - - - - - -
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    Outdated

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    Note that this example won't work with the latest version of ydata-synthetic.

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    Please check ydata-sdk to see how to generate synthetic time-series data.

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    +

    Why YData Fabric vs TimeGAN for time-series data

    +

    YData Fabric offers advanced capabilities for time-series synthetic data generation, surpassing TimeGAN in terms of flexibility, +scalability, and ease of use. With YData Fabric, users can generate high-quality synthetic time-series data while benefiting from built-in data profiling tools +that ensure the integrity and consistency of the data. Unlike TimeGAN, which is a single model for time-series, YData Fabric offers a solution that is suitable for different types of datasets and behaviours. +Additionally, YData Fabric is designed for scalability, enabling seamless handling of large, complex time-series datasets. Its guided UI makes it easy to adapt to different time-series scenarios, +from healthcare to financial data, making it a more comprehensive and flexible solution for time-series data generation.

    +

    For more on YData Fabric vs Synthetic data generation with TimeGAN read this blogpost.

    +

    Using TimeGAN to generate synthetic time-series data

    Although tabular data may be the most frequently discussed type of data, a great number of real-world domains — from traffic and daily trajectories to stock prices and energy consumption patterns — produce time-series data which introduces several aspects of complexity to synthetic data generation.

    Time-series data is structured sequentially, with observations ordered chronologically based on their associated timestamps or time intervals. It explicitly incorporates the temporal aspect, allowing for the analysis of trends, seasonality, and other dependencies over time.

    TimeGAN is a model that uses a Generative Adversarial Network (GAN) framework to generate synthetic time series data by learning the underlying temporal dependencies and characteristics of the original data:

    diff --git a/1.4/reference/api/index.html b/1.4/synthetic_data/ydata_fabric_app/index.html similarity index 67% rename from 1.4/reference/api/index.html rename to 1.4/synthetic_data/ydata_fabric_app/index.html index c91d758a..1b2fbf50 100644 --- a/1.4/reference/api/index.html +++ b/1.4/synthetic_data/ydata_fabric_app/index.html @@ -9,13 +9,17 @@ + + + + - Index - YData-Synthetic + UI interface - YData Fabric - YData-Synthetic @@ -111,6 +115,11 @@
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    The UI guided experience for Synthetic Data generation

    +

    YData Fabric provides a robust, guided user interface (UI) specifically designed to streamline synthetic data generation. +This interface is tailored to support users at every level, ensuring that both novice users and experienced data scientists can efficiently generate +synthetic datasets while adhering to best practices.

    +

    Step-by-Step Workflow

    +

    The YData Fabric UI organizes the synthetic data generation process into a structured, step-by-step workflow. +Each stage of the process is clearly defined and supported by guidance within the interface, helping users navigate tasks like data profiling, +metadata and synthesizer configuration and synthetic data quality evaluation.

    +
      +
    • Data Upload and Profiling: Users start by uploading their datasets directly into the platform. YData Fabric’s profiling tool automatically scans +the data, generating insights into key attributes such as data distributions, correlations, and missing values. +These insights are presented in an intuitive, visual format, ensuring users can quickly assess the quality and structure of their data.
      • +
    • Alerts for Data Issues: The UI will alert users to potential issues such as data imbalances, outliers, or incomplete fields that may affect the +quality of the synthetic data.
      • +
    • Synthetic Data Generation Model Configuration: Once the data is profiled, the UI supports metadata configuration (categorical, numerical, dates, etc), +anonymization integration.
      • +
    • Model Performance Insights: During the model training phase, YData Fabric monitors key performance indicators (KPIs) like fidelity, utility and privacy. +These KPIs, such as data fidelity and privacy scores, are displayed on the dashboard, allowing users to evaluate how closely the synthetic data aligns with the original dataset.
      • +
    • Customization and Advanced Controls: For more experienced users, YData Fabric provides customization options within the guided UI. +Users have access to advanced settings, such as conditional synthetic data generation or business rules.
      • +
    • Preserving Data Integrity: For datasets requiring strict adherence to structural patterns (e.g., time-series data, healthcare records or databases).
      • +
    +

    Getting started with YData Fabric (Community version)

    +

    YData Fabric’s Community Version offers users a free, accessible entry point to explore synthetic data generation. +To get started, users can sign up for the Community Version and access the guided UI directly. +Once registered, users are provided with a range of features, including data profiling, synthetic data generation, pipelines and access to YData’s proprietary models for data quality!

    diff --git a/latest/getting-started/quickstart/index.html b/latest/getting-started/quickstart/index.html deleted file mode 100644 index 9e1fef98..00000000 --- a/latest/getting-started/quickstart/index.html +++ /dev/null @@ -1,16 +0,0 @@ - - - - - Redirecting - - - - - Redirecting to ../../../1.4/getting-started/quickstart/... - - \ No newline at end of file diff --git a/latest/reference/api/index.html b/latest/reference/api/index.html deleted file mode 100644 index 2f91127b..00000000 --- a/latest/reference/api/index.html +++ /dev/null @@ -1,16 +0,0 @@ - - - - - Redirecting - - - - - Redirecting to ../../../1.4/reference/api/... - - \ No newline at end of file diff --git a/latest/reference/api/preprocessing/base/index.html b/latest/reference/api/preprocessing/base/index.html deleted file mode 100644 index fc341c53..00000000 --- a/latest/reference/api/preprocessing/base/index.html +++ /dev/null @@ -1,16 +0,0 @@ - - - - - Redirecting - - - - - Redirecting to ../../../../../1.4/reference/api/preprocessing/base/... - 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