def check_fit(self):
"""Assert that the object is fit
Raises a `NotFittedError` if the model is not fitted.
"""
if not self.fitted:
raise NotFittedError("This model is not fitted.")
def check_fit(self):
"""Assert that the model is fit and the computed `theta` is valid.
Raises a `NotFittedError` if the model is not fitted.
Raises a `ValueError` if the computed theta is invalid."""
if not self.theta:
raise NotFittedError("This model is not fitted.")
self.check_theta()
def sample_all(self, num_rows=5):
"""Sample the whole dataset.
Args:
num_rows (int): Amount of rows to sample.
"""
if self.sampler is None:
raise NotFittedError('SDV instance has not been fitted')
return self.sampler.sample_all(num_rows)
def sample_table(self, table_name):
"""Samples the given table to its original size.
Args:
table_name (str): Table to sample.
"""
if self.sampler is None:
raise NotFittedError('SDV instance has not been fitted')
return self.sampler.sample_table(table_name)
def check_fit(self):
"""Check whether this model has already been fit to a random variable.
Raise a ``NotFittedError`` if it has not.
Raises:
NotFittedError:
if the model is not fitted.
"""
if not self.fitted:
raise NotFittedError("This model is not fitted.")
def sample_rows(self, table_name, num_rows):
"""Sample `num_rows` rows from the given table.
Args:
table_name(str): Name of the table to sample from.
num_rows(int): Amount of rows to sample.
"""
if self.sampler is None:
raise NotFittedError('SDV instance has not been fitted')
return self.sampler.sample_rows(table_name, num_rows)
def sample_all(self, num_rows=5, reset_primary_keys=False):
"""Sample the whole dataset.
Args:
num_rows(int): Amount of rows to sample.
reset_primary_keys(bool): Wheter or not reset the pk generators.
"""
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 sample_table(self, table_name, reset_primary_keys=False):
"""Samples the given table to its original size.
Args:
table_name (str): Table to sample.
reset_primary_keys(bool): Wheter or not reset the pk generators.
"""
if self.sampler is None:
raise NotFittedError('SDV instance has not been fitted')
return self.sampler.sample_table(table_name,
reset_primary_keys=reset_primary_keys)
def sample_rows(self, table_name, num_rows, reset_primary_keys=False):
"""Sample `num_rows` rows from the given table.
Args:
table_name(str): Name of the table to sample from.
num_rows(int): Amount of rows to sample.
reset_primary_keys(bool): Wheter or not reset the pk generators.
"""
if self.sampler is None:
raise NotFittedError('SDV instance has not been fitted')
return self.sampler.sample_rows(table_name,
num_rows,
reset_primary_keys=reset_primary_keys)