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MultiTable

Bases: BaseSynthesizer

MultiTable synthesizer class.

Methods

  • fit: train a synthesizer instance.
  • sample: request synthetic data.
  • status: current status of the synthesizer instance.
Note

The synthesizer instance is created in the backend only when the fit method is called.

Parameters:

Name Type Description Default
write_connector UID | Connector

Connector of type RDBMS to be used to write the samples

 required
uid UID

(optional) UID to identify this synthesizer

 None
name str

(optional) Name to be used when creating the synthesizer. Calculated internally if not provided

 None
client Client

(optional) Client to connect to the backend

 None
Source code in ydata/sdk/synthesizers/multitable.py 
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class MultiTableSynthesizer(BaseSynthesizer):
    """MultiTable synthesizer class.

    Methods
    -------
    - `fit`: train a synthesizer instance.
    - `sample`: request synthetic data.
    - `status`: current status of the synthesizer instance.

    Note:
            The synthesizer instance is created in the backend only when the `fit` method is called.

    Arguments:
        write_connector (UID | Connector): Connector of type RDBMS to be used to write the samples
        uid (UID): (optional) UID to identify this synthesizer
        name (str): (optional) Name to be used when creating the synthesizer. Calculated internally if not provided
        client (Client): (optional) Client to connect to the backend
    """

    def __init__(
            self, write_connector: Union[Connector, UID], uid: Optional[UID] = None, name: Optional[str] = None,
            project: Optional[Project] = None, client: Optional[Client] = None):

        super().__init__(uid, name, project, client)

        connector = self._check_or_fetch_connector(write_connector)
        self.__write_connector = connector.uid

    def fit(self, X: DataSource,
            privacy_level: PrivacyLevel = PrivacyLevel.HIGH_FIDELITY,
            datatype: Optional[Union[DataSourceType, str]] = None,
            sortbykey: Optional[Union[str, List[str]]] = None,
            entities: Optional[Union[str, List[str]]] = None,
            generate_cols: Optional[List[str]] = None,
            exclude_cols: Optional[List[str]] = None,
            dtypes: Optional[Dict[str, Union[str, DataType]]] = None,
            target: Optional[str] = None,
            anonymize: Optional[dict] = None,
            condition_on: Optional[List[str]] = None) -> None:
        """Fit the synthesizer.

        The synthesizer accepts as training dataset a YData [`DataSource`][ydata.sdk.datasources.DataSource].
        Except X, all the other arguments are for now ignored until they are supported.

        Arguments:
            X (DataSource): DataSource to Train
        """

        self._fit_from_datasource(X, datatype=DataSourceType.MULTITABLE)

    def sample(self, frac: Union[int, float] = 1, write_connector: Optional[Union[Connector, UID]] = None) -> None:
        """Sample from a [`MultiTableSynthesizer`][ydata.sdk.synthesizers.MultiTableSynthesizer]
        instance.
        The sample is saved in the connector that was provided in the synthesizer initialization
        or in the

        Arguments:
            frac (int | float): fraction of the sample to be returned
        """

        assert frac >= 0.1, InputError(
            "It is not possible to generate an empty synthetic data schema. Please validate the input provided. ")
        assert frac <= 5, InputError(
            "It is not possible to generate a database that is 5x bigger than the original dataset. Please validate the input provided.")

        payload = {
            'fraction': frac,
        }

        if write_connector is not None:
            connector = self._check_or_fetch_connector(write_connector)
            payload['writeConnector'] = connector.uid

        response = self._client.post(
            f"/synthesizer/{self.uid}/sample", json=payload, project=self._project)

        data = response.json()
        sample_uid = data.get('uid')
        sample_status = None
        while sample_status not in ['finished', 'failed']:
            self._logger.info('Sampling from the synthesizer...')
            response = self._client.get(
                f'/synthesizer/{self.uid}/history', project=self._project)
            history = response.json()
            sample_data = next((s for s in history if s.get('uid') == sample_uid), None)
            sample_status = sample_data.get('status', {}).get('state')
            sleep(BACKOFF)

        print(
            f"Sample created and saved into connector with ID {self.__write_connector or write_connector}")

    def _create_payload(self) -> dict:
        payload = super()._create_payload()
        payload['writeConnector'] = self.__write_connector

        return payload

    def _check_or_fetch_connector(self, write_connector: Union[Connector, UID]) -> Connector:
        self._logger.debug(f'Write connector is {write_connector}')
        if isinstance(write_connector, str):
            self._logger.debug(f'Write connector is of type `UID` {write_connector}')
            write_connector = Connector.get(write_connector)
            self._logger.debug(f'Using fetched connector {write_connector}')

        if write_connector.uid is None:
            raise InputError("Invalid connector provided as input for write")

        if write_connector.type not in [ConnectorType.AZURE_SQL, ConnectorType.MYSQL, ConnectorType.SNOWFLAKE]:
            raise ConnectorError(
                f"Invalid type `{write_connector.type}` for the provided connector")

        return write_connector

fit(X, privacy_level=PrivacyLevel.HIGH_FIDELITY, datatype=None, sortbykey=None, entities=None, generate_cols=None, exclude_cols=None, dtypes=None, target=None, anonymize=None, condition_on=None)

Fit the synthesizer.

The synthesizer accepts as training dataset a YData DataSource. Except X, all the other arguments are for now ignored until they are supported.

Parameters:

Name Type Description Default
X DataSource

DataSource to Train

 required
Source code in ydata/sdk/synthesizers/multitable.py 
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def fit(self, X: DataSource,
        privacy_level: PrivacyLevel = PrivacyLevel.HIGH_FIDELITY,
        datatype: Optional[Union[DataSourceType, str]] = None,
        sortbykey: Optional[Union[str, List[str]]] = None,
        entities: Optional[Union[str, List[str]]] = None,
        generate_cols: Optional[List[str]] = None,
        exclude_cols: Optional[List[str]] = None,
        dtypes: Optional[Dict[str, Union[str, DataType]]] = None,
        target: Optional[str] = None,
        anonymize: Optional[dict] = None,
        condition_on: Optional[List[str]] = None) -> None:
    """Fit the synthesizer.

    The synthesizer accepts as training dataset a YData [`DataSource`][ydata.sdk.datasources.DataSource].
    Except X, all the other arguments are for now ignored until they are supported.

    Arguments:
        X (DataSource): DataSource to Train
    """

    self._fit_from_datasource(X, datatype=DataSourceType.MULTITABLE)

sample(frac=1, write_connector=None)

Sample from a MultiTableSynthesizer instance. The sample is saved in the connector that was provided in the synthesizer initialization or in the

Parameters:

Name Type Description Default
frac int | float

fraction of the sample to be returned

 1
Source code in ydata/sdk/synthesizers/multitable.py 
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def sample(self, frac: Union[int, float] = 1, write_connector: Optional[Union[Connector, UID]] = None) -> None:
    """Sample from a [`MultiTableSynthesizer`][ydata.sdk.synthesizers.MultiTableSynthesizer]
    instance.
    The sample is saved in the connector that was provided in the synthesizer initialization
    or in the

    Arguments:
        frac (int | float): fraction of the sample to be returned
    """

    assert frac >= 0.1, InputError(
        "It is not possible to generate an empty synthetic data schema. Please validate the input provided. ")
    assert frac <= 5, InputError(
        "It is not possible to generate a database that is 5x bigger than the original dataset. Please validate the input provided.")

    payload = {
        'fraction': frac,
    }

    if write_connector is not None:
        connector = self._check_or_fetch_connector(write_connector)
        payload['writeConnector'] = connector.uid

    response = self._client.post(
        f"/synthesizer/{self.uid}/sample", json=payload, project=self._project)

    data = response.json()
    sample_uid = data.get('uid')
    sample_status = None
    while sample_status not in ['finished', 'failed']:
        self._logger.info('Sampling from the synthesizer...')
        response = self._client.get(
            f'/synthesizer/{self.uid}/history', project=self._project)
        history = response.json()
        sample_data = next((s for s in history if s.get('uid') == sample_uid), None)
        sample_status = sample_data.get('status', {}).get('state')
        sleep(BACKOFF)

    print(
        f"Sample created and saved into connector with ID {self.__write_connector or write_connector}")