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Extract stanfit from model object in Py following cmdstanpy refactoring (#1353)

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* add self.stan_fit in fit methods

* small changes to fix tests

* make changes to remove self.params from backend classes
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Ryan Nazareth 2020-03-07 02:11:04 +00:00 committed by GitHub
parent 708ae20c04
commit 0ca50cfb7f
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GPG key ID: 4AEE18F83AFDEB23
1 changed files with 16 additions and 13 deletions
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29
python/fbprophet/models.py
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@ -19,6 +19,7 @@ class IStanBackend(ABC):
def __init__(self, logger):
self.model = self.load_model()
self.logger = logger
self.stan_fit = None
@staticmethod
@abstractmethod
@ -74,7 +75,7 @@ class CmdStanPyBackend(IStanBackend):
kwargs['algorithm'] = 'Newton' if stan_data['T'] < 100 else 'LBFGS'
iterations = int(1e4)
try:
stan_fit = self.model.optimize(data=stan_data,
self.stan_fit = self.model.optimize(data=stan_data,
inits=stan_init,
iter=iterations,
**kwargs)
@ -85,14 +86,14 @@ class CmdStanPyBackend(IStanBackend):
'Optimization terminated abnormally. Falling back to Newton.'
)
kwargs['algorithm'] = 'Newton'
stan_fit = self.model.optimize(data=stan_data,
self.stan_fit = self.model.optimize(data=stan_data,
inits=stan_init,
iter=iterations,
**kwargs)
else:
raise e
params = self.stan_to_dict_numpy(stan_fit.column_names, stan_fit.optimized_params_np)
params = self.stan_to_dict_numpy(self.stan_fit.column_names, self.stan_fit.optimized_params_np)
for par in params:
params[par] = params[par].reshape((1, -1))
return params
@ -105,14 +106,14 @@ class CmdStanPyBackend(IStanBackend):
if 'warmup_iters' not in kwargs:
kwargs['warmup_iters'] = samples // 2
stan_fit = self.model.sample(data=stan_data,
self.stan_fit = self.model.sample(data=stan_data,
inits=stan_init,
sampling_iters=samples,
**kwargs)
res = stan_fit.sample
res = self.stan_fit.sample
(samples, c, columns) = res.shape
res = res.reshape((samples * c, columns))
params = self.stan_to_dict_numpy(stan_fit.column_names, res)
params = self.stan_to_dict_numpy(self.stan_fit.column_names, res)
for par in params:
s = params[par].shape
@ -220,10 +221,10 @@ class PyStanBackend(IStanBackend):
iter=samples,
)
args.update(kwargs)
stan_fit = self.model.sampling(**args)
self.stan_fit = self.model.sampling(**args)
out = dict()
for par in stan_fit.model_pars:
out[par] = stan_fit[par]
for par in self.stan_fit.model_pars:
out[par] = self.stan_fit[par]
# Shape vector parameters
if par in ['delta', 'beta'] and len(out[par].shape) < 2:
out[par] = out[par].reshape((-1, 1))
@ -239,17 +240,19 @@ class PyStanBackend(IStanBackend):
)
args.update(kwargs)
try:
params = self.model.optimizing(**args)
self.stan_fit = self.model.optimizing(**args)
except RuntimeError:
# Fall back on Newton
self.logger.warning(
'Optimization terminated abnormally. Falling back to Newton.'
)
args['algorithm'] = 'Newton'
params = self.model.optimizing(**args)
self.stan_fit = self.model.optimizing(**args)
for par in params:
params[par] = params[par].reshape((1, -1))
params = dict()
for par in self.stan_fit.keys():
params[par] = self.stan_fit[par].reshape((1, -1))
return params