def samples(self,
model: Model,
n_samples: int,
n_burn: int,
thin: float = 0.1,
store_in_sample_predictions: bool = True,
store_acceptance: bool = True) -> Chain:
print("Starting burn")
trace_logger = self.trace_logger_class()
for _ in tqdm(range(n_burn)):
self.step(model, trace_logger)
trace = []
model_trace = []
acceptance_trace = []
print("Starting sampling")
thin_inverse = 1. / thin
for ss in tqdm(range(n_samples)):
step_trace_dict = self.step(model, trace_logger)
if ss % thin_inverse == 0:
if store_in_sample_predictions:
in_sample_log = trace_logger["In Sample Prediction"](
model.predict())
if in_sample_log is not None:
trace.append(in_sample_log)
if store_acceptance:
acceptance_trace.append(step_trace_dict)
model_log = trace_logger["Model"](model)
if model_log is not None:
model_trace.append(model_log)
return {
"model": model_trace,
"acceptance": acceptance_trace,
"in_sample_predictions": trace
}
def samples(
self,
model: Model,
n_samples: int,
n_burn: int,
thin: float = 0.1,
store_in_sample_predictions: bool = True
) -> Tuple[List[Model], np.ndarray]:
print("Starting burn")
for _ in tqdm(range(n_burn)):
self.step(model)
trace = []
model_trace = []
print("Starting sampling")
thin_inverse = 1. / thin
for ss in tqdm(range(n_samples)):
self.step(model)
if ss % thin_inverse == 0:
if store_in_sample_predictions:
trace.append(model.predict())
model_trace.append(deep_copy_model(model))
return model_trace, np.array(trace)