Metadata

This guide describes the SDGym's metadata specification for a single table of data.

What is metadata?

Metadata is a basic, factual description of a dataset that includes:

• The type of data that each column represents

• The primary keys and other identifiers of the table

How does the SDGym library use metadata? Many of the synthesizer use information in the metadata to create higher quality synthetic data. For example, the SDV Synthesizers apply different logic to different column types.

Additionally, the evaluation framework factors in the metadata when applying metrics. For example, some metrics may only be applicable for specific column types.

The SDGym library expects that every dataset will have corresponding metadata provided as a JSON file. During benchmarking, the SDGym reads the file as a Python dictionary.

Example

We assume that the data is present in a CSV format that describes rows and columns of a single table.

This example of a single table includes a new row for each user. The row includes their personal information.

Click to see the table's metadata

This is the metadata dictionary for the illustrated table.

{
    "METADATA_SPEC_VERSION": "V1",
    "tables": {
        "users": {
            "primary_key": "user_id",
            "columns": {
                "user_id": { "sdtype": "id", "regex": "U_[0-9]{3}" },
                "age": { "sdtype": "numerical" },
                "address": { "sdtype": "street_address" }, 
                "tier": { "sdtype": "categorical" },
                "active": { "sdtype": "boolean" },
                "paid_amt": { "sdtype": "numerical" },
                "renew_date": { "sdtype": "datetime", "datetime_format": "%Y-%m-%d" }
            }
        }
    }
}​

Overview

The metadata for a single table contains the following elements:

• (required) "METADATA_SPEC_VERSION": The version of the metadata. If you are using this, the metadata version will be "V1", indicating that it is a multi table dataset that is compatible with SDV version 1.

• (required) "tables": A dictionary that maps the table names to the table-specific metadata such as primary keys, column names and data types. Note that SDGym only works with single-table schemas.

Tables

The tables dictionary maps each table name to the table-specific metadata. Because SDGym only works with single-table schemas, the table name does not matter. But please be sure that the table-specific metadata matches your data.

• (required) "columns": A dictionary that maps the column names to the data types they represent and any other attributes.

• "primary_key": The column name that is the primary key in the table

• "alternate_keys": A list of column names that can act as alternate keys in the table

Table Columns

When describing a column, you will provide the column name and the data type, known as the sdtype.

The 5 common sdtypes are: "numerical", "datetime", "categorical", "boolean" and "id". Click on the type below to learn more about the type and how to specify it in the metadata.

Table Metadata

Each table in the metadata has two keys:

• "primary_key": the column name used to identify a row in the table

• (required) "columns": a dictionary description of each column

boolean

Boolean columns represent True or False values.

"active" : {
    "sdtype": "boolean"
}

Properties (None)

categorical

Categorical columns represent discrete data

"tier" : {
    "sdtype": "categorical"
}

Properties (None)

datetime

Date columns represent a point in time

"renew_date": {
    "sdtype": "datetime", 
    "datetime_format": "%Y-%m-%d"
}

Properties

• (required) datetime_format: A string describing the format as defined by Python's strftime module.

numerical

Numerical columns represents discrete or continuous numerical values.

"age": {
    "sdtype": "numerical",
    "computer_representation": "Int64"
},
"paid_amt": {
    "sdtype": "numerical",
    "computer_representation": "Float"
}

Properties

• computer_representation: A string that represents how you'll ultimately store the data. This determines the min and max values allowed Available options are: 'Float', 'Int8', 'Int16', 'Int32', 'Int64', 'UInt8', 'UInt16', 'UInt32', 'UInt64'

Use "type": "numerical" to specify columns that represent whole number or continuous values

id

ID columns represent identifiers that do not have any special mathematical or semantic meaning

"user_id": { 
    "sdtype": "id",
    "regex_format": "U_[0-9]{3}"
}

Properties

• regex_format: A string describing the format of the ID as a regular expression

other

You can input any other data type such as 'phone_number', 'ssn' or 'email'. See the Sdtypes Reference for a full list.

"address": {
    "sdtype": "address",
    "pii": true
}

Properties

• pii: A boolean denoting whether the data is sensitive

  • (default) true: The column is sensitive, meaning the values should be anonymized

  • false: The column is not sensitive, meaning the exact set of values can be reused in the synthetic data

FAQs

Should all the datasets include metadata?

Yes, every dataset available in the SDGym's demo module has an associated metadata file. If you are supplying custom datasets, make sure to write an attach a metadata file too. See Datasets for more information.

Can my custom synthesizer make use of the metadata?

Yes, your custom synthesizer can use any information in the metadata to help create higher quality synthetic data. The metadata information is passed into your synthesizer as a Python dictionary during the training process. See Custom Synthesizers for more information.