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You are looking at the documentation for an older version of the SDV! We are no longer supporting or maintaining this version of the software
Click here to go to the new docs pages.
sdv.relational.hma.
HMA1
Hierarchical Modeling Algorithm One.
metadata (dict, str or Metadata) – Metadata dict, path to the metadata JSON file or Metadata instance itself.
root_path (str or None) – Path to the dataset directory. If None and metadata is a path, the metadata location is used. If None and metadata is a dict, the current working directory is used.
None
model (type) – Class of the copula to use. Defaults to sdv.models.copulas.GaussianCopula.
copula
sdv.models.copulas.GaussianCopula
model_kwargs (dict) – Keyword arguments to pass to the model. If the default model is used, this defaults to using a gaussian distribution and a FrequencyEncoder_noised transformer.
gaussian
FrequencyEncoder_noised
__init__
Initialize self. See help(type(self)) for accurate signature.
Methods
__init__(metadata[, root_path, model, …])
Initialize self.
fit([tables])
fit
Fit this relational model instance to the dataset data.
load(path)
load
Load a model from a given path.
sample([table_name, num_rows, …])
sample
Generate synthetic data for one table or the entire dataset.
save(path)
save
Save this instance to the given path using cloudpickle.
Attributes
DEFAULT_MODEL_KWARGS
metadata