def generate(self, batch_size=1, outputs=None, with_values=None):
"""Generate a batch of outputs using the global numpy seed.
This method is useful for testing that the ELFI graph works.
Parameters
----------
batch_size : int, optional
outputs : list, optional
with_values : dict, optional
You can specify values for nodes to use when generating data
"""
if outputs is None:
outputs = self.source_net.nodes()
elif isinstance(outputs, str):
outputs = [outputs]
if not isinstance(outputs, list):
raise ValueError('Outputs must be a list of node names')
pool = None
if with_values is not None:
pool = OutputPool(with_values.keys())
pool.add_batch(with_values, 0)
context = ComputationContext(batch_size, seed='global', pool=pool)
client = elfi.client.get_client()
compiled_net = client.compile(self.source_net, outputs)
loaded_net = client.load_data(compiled_net, context, batch_index=0)
return client.compute(loaded_net)
def generate(self, batch_size=1, outputs=None, with_values=None):
"""Generates a batch of outputs using the global seed.
This method is useful for testing that the generative model works.
Parameters
----------
batch_size : int
outputs : list
with_values : dict
You can specify values for nodes to use when generating data
"""
if outputs is None:
outputs = self.source_net.nodes()
elif isinstance(outputs, str):
outputs = [outputs]
if not isinstance(outputs, list):
raise ValueError('Outputs must be a list of node names')
context = self.computation_context.copy()
# Use the global random_state
context.seed = 'global'
context.batch_size = batch_size
if with_values is not None:
pool = OutputPool(with_values.keys())
pool.add_batch(with_values, 0)
context.pool = pool
client = elfi.client.get_client()
compiled_net = client.compile(self.source_net, outputs)
loaded_net = client.load_data(compiled_net, context, batch_index=0)
return client.compute(loaded_net)