sdv.tabular.copulas.GaussianCopula

class sdv.tabular.copulas.GaussianCopula(field_names=None, field_types=None, field_transformers=None, anonymize_fields=None, primary_key=None, constraints=None, table_metadata=None, field_distributions=None, default_distribution=None, categorical_transformer=None, rounding='auto', min_value='auto', max_value='auto')[source]

Model wrapping copulas.multivariate.GaussianMultivariate copula.

Parameters
  • field_names (list[str]) – List of names of the fields that need to be modeled and included in the generated output data. Any additional fields found in the data will be ignored and will not be included in the generated output. If None, all the fields found in the data are used.

  • field_types (dict[str, dict]) – Dictinary specifying the data types and subtypes of the fields that will be modeled. Field types and subtypes combinations must be compatible with the SDV Metadata Schema.

  • field_transformers (dict[str, str]) –

    Dictinary specifying which transformers to use for each field. Available transformers are:

    • integer: Uses a NumericalTransformer of dtype int.

    • float: Uses a NumericalTransformer of dtype float.

    • categorical: Uses a CategoricalTransformer without gaussian noise.

    • categorical_fuzzy: Uses a CategoricalTransformer adding gaussian noise.

    • one_hot_encoding: Uses a OneHotEncodingTransformer.

    • label_encoding: Uses a LabelEncodingTransformer.

    • boolean: Uses a BooleanTransformer.

    • datetime: Uses a DatetimeTransformer.

  • anonymize_fields (dict[str, str]) – Dict specifying which fields to anonymize and what faker category they belong to.

  • primary_key (str) – Name of the field which is the primary key of the table.

  • constraints (list[Constraint, dict]) – List of Constraint objects or dicts.

  • table_metadata (dict or metadata.Table) – Table metadata instance or dict representation. If given alongside any other metadata-related arguments, an exception will be raised. If not given at all, it will be built using the other arguments or learned from the data.

  • field_distributions (dict) –

    Dictionary that maps field names from the table that is being modeled with the distribution that needs to be used. The distributions can be passed as either a copulas.univariate instance or as one of the following values:

    • univariate: Let copulas select the optimal univariate distribution. This may result in non-parametric models being used.

    • parametric: Let copulas select the optimal univariate distribution, but restrict the selection to parametric distributions only.

    • bounded: Let copulas select the optimal univariate distribution, but restrict the selection to bounded distributions only. This may result in non-parametric models being used.

    • semi_bounded: Let copulas select the optimal univariate distribution, but restrict the selection to semi-bounded distributions only. This may result in non-parametric models being used.

    • parametric_bounded: Let copulas select the optimal univariate distribution, but restrict the selection to parametric and bounded distributions only.

    • parametric_semi_bounded: Let copulas select the optimal univariate distribution, but restrict the selection to parametric and semi-bounded distributions only.

    • gaussian: Use a Gaussian distribution.

    • gamma: Use a Gamma distribution.

    • beta: Use a Beta distribution.

    • student_t: Use a Student T distribution.

    • gaussian_kde: Use a GaussianKDE distribution. This model is non-parametric, so using this will make get_parameters unusable.

    • truncated_gaussian: Use a Truncated Gaussian distribution.

  • default_distribution (copulas.univariate.Univariate or str) – Copulas univariate distribution to use by default. To choose from the list of possible field_distribution values. Defaults to parametric.

  • categorical_transformer (str) –

    Type of transformer to use for the categorical variables, which must be one of the following values:

    • one_hot_encoding: Apply a OneHotEncodingTransformer to the categorical column, which replaces the column with one boolean column for each possible category, indicating whether each row had that value or not.

    • label_encoding: Apply a LabelEncodingTransformer, which replaces the value of each category with an integer value that acts as its label.

    • categorical: Apply CategoricalTransformer, which replaces each categorical value with a float number in the [0, 1] range which is inversely proportional to the frequency of that category.

    • categorical_fuzzy: Apply a CategoricalTransformer with the fuzzy argument set to True, which makes it add gaussian noise around each value.

  • rounding (int, str or None) – Define rounding scheme for NumericalTransformer. If set to an int, values will be rounded to that number of decimal places. If None, values will not be rounded. If set to 'auto', the transformer will round to the maximum number of decimal places detected in the fitted data. Defaults to 'auto'.

  • min_value (int, str or None) – Specify the minimum value the NumericalTransformer should use. If an integer is given, sampled data will be greater than or equal to it. If the string 'auto' is given, the minimum will be the minimum value seen in the fitted data. If None is given, there won’t be a minimum. Defaults to 'auto'.

  • max_value (int, str or None) – Specify the maximum value the NumericalTransformer should use. If an integer is given, sampled data will be less than or equal to it. If the string 'auto' is given, the maximum will be the maximum value seen in the fitted data. If None is given, there won’t be a maximum. Defaults to 'auto'.

__init__(field_names=None, field_types=None, field_transformers=None, anonymize_fields=None, primary_key=None, constraints=None, table_metadata=None, field_distributions=None, default_distribution=None, categorical_transformer=None, rounding='auto', min_value='auto', max_value='auto')[source]

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__([field_names, field_types, …])

Initialize self.

fit(data)

Fit this model to the data.

get_distributions()

Get the marginal distributions used by this copula.

get_likelihood(table_data)

Get the likelihood of each row belonging to this table.

get_metadata()

Get metadata about the table.

get_parameters()

Get the parameters learned from the data.

load(path)

Load a TabularModel instance from a given path.

sample([num_rows, max_retries, …])

Sample rows from this table.

save(path)

Save this model instance to the given path using pickle.

set_parameters(parameters)

Regenerate a previously learned model from its parameters.