def test_sample_no_sample_children(self):
"""Test sample no sample children"""
# Setup
sampler = Mock(spec=Sampler)
sampler.models = {'test': 'model'}
sampler.metadata.get_parents.return_value = None
# Run
Sampler.sample(sampler, 'test', 5, sample_children=False)
def test_sample_no_sample_children(self):
"""Test sample no sample children"""
# Setup
sampler = Mock(spec=Sampler)
sampler.models = {'test': 'model'}
sampler.metadata.get_parents.return_value = None
# Run
Sampler.sample(sampler, 'test', 5, sample_children=False)
sampler._transform_synthesized_rows.assert_called_once_with(
sampler._sample_rows.return_value, 'test')
def test_sample_no_sample_children(self):
"""Test sample no sample children"""
# Setup
models = {'test': 'model'}
# Run
sampler = Mock()
sampler.models = models
sampler.metadata.get_parents.return_value = None
table_name = 'test'
num_rows = 5
Sampler.sample(sampler, table_name, num_rows, sample_children=False)
def test_sample_table_with_parents(self):
"""Test sample table with parents."""
sampler = Mock(spec=Sampler)
sampler.metadata = Mock(spec=Metadata)
sampler.metadata.get_parents.return_value = ['test_parent']
sampler.metadata.get_foreign_key.return_value = 'id'
sampler.models = {'test': 'some model'}
sampler._get_primary_keys.return_value = None, pd.Series({'id': 0})
sampler._sample_rows.return_value = pd.DataFrame({'id': [0, 1]})
Sampler.sample(sampler, 'test', 5)
sampler.metadata.get_parents.assert_called_once_with('test')
sampler.metadata.get_foreign_key.assert_called_once_with(
'test_parent', 'test')
class SDV:
"""Automated generative modeling and sampling tool.
Allows the users to generate synthetic data after creating generative models for their data.
Args:
model (type):
Class of the ``copula`` to use. Defaults to
``sdv.models.copulas.GaussianCopula``.
model_kwargs (dict):
Keyword arguments to pass to the model. Defaults to ``None``.
"""
sampler = None
def __init__(self, model=DEFAULT_MODEL, model_kwargs=None):
self.model = model
if model_kwargs is None:
self.model_kwargs = DEFAULT_MODEL_KWARGS.copy()
else:
self.model_kwargs = model_kwargs
def fit(self, metadata, tables=None, root_path=None):
"""Fit this SDV instance to the dataset data.
Args:
metadata (dict, str or Metadata):
Metadata dict, path to the metadata JSON file or Metadata instance itself.
tables (dict):
Dictionary with the table names as key and ``pandas.DataFrame`` instances as
values. If ``None`` is given, the tables will be loaded from the paths
indicated in ``metadata``. Defaults to ``None``.
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.
"""
if isinstance(metadata, Metadata):
self.metadata = metadata
else:
self.metadata = Metadata(metadata, root_path)
self.metadata.validate(tables)
self.modeler = Modeler(self.metadata, self.model, self.model_kwargs)
self.modeler.model_database(tables)
self.sampler = Sampler(self.metadata, self.modeler.models, self.model,
self.model_kwargs)
def sample(self,
table_name,
num_rows,
sample_children=True,
reset_primary_keys=False):
"""Sample ``num_rows`` rows from the indicated table.
Args:
table_name (str):
Name of the table to sample from.
num_rows (int):
Amount of rows to sample.
sample_children (bool):
Whether or not to sample children tables. Defaults to ``True``.
reset_primary_keys (bool):
Wheter or not reset the primary key generators. Defaults to ``False``.
Returns:
pandas.DataFrame:
Sampled data with the number of rows specified in ``num_rows``.
Raises:
NotFittedError:
A ``NotFittedError`` is raised when the ``SDV`` instance has not been fitted yet.
"""
if self.sampler is None:
raise NotFittedError('SDV instance has not been fitted')
return self.sampler.sample(table_name,
num_rows,
sample_children=sample_children,
reset_primary_keys=reset_primary_keys)
def sample_all(self, num_rows=5, reset_primary_keys=False):
"""Sample the entire dataset.
Args:
num_rows (int):
Amount of rows to sample. Defaults to ``5``.
reset_primary_keys (bool):
Wheter or not reset the primary key generators. Defaults to ``False``.
Returns:
dict:
Tables sampled.
Raises:
NotFittedError:
A ``NotFittedError`` is raised when the ``SDV`` instance has not been fitted yet.
"""
if self.sampler is None:
raise NotFittedError('SDV instance has not been fitted')
return self.sampler.sample_all(num_rows,
reset_primary_keys=reset_primary_keys)
def save(self, path):
"""Save this SDV instance to the given path using pickle.
Args:
path (str):
Path where the SDV instance will be serialized.
"""
with open(path, 'wb') as output:
pickle.dump(self, output)
@classmethod
def load(cls, path):
"""Load a SDV instance from a given path.
Args:
path (str):
Path from which to load the SDV instance.
"""
with open(path, 'rb') as f:
return pickle.load(f)
