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Use VB estimates as initial values for MCMC sampling by default #96

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merged 5 commits into from
Aug 19, 2019
Merged

Use VB estimates as initial values for MCMC sampling by default #96

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523c132
Make it possible to use VB estimates as initial values
JaeyeongYang Aug 8, 2019
2928934
Set VB estimates as initial values by default
JaeyeongYang Aug 8, 2019
dec556d
Implement to use VB estimates as initial values
JaeyeongYang Aug 16, 2019
759078b
Use VB estimates as initial values by default for Python
JaeyeongYang Aug 16, 2019
17c66b6
Merge branch 'develop' into feature/use-vb-as-initial-values
JaeyeongYang Aug 19, 2019
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2 changes: 1 addition & 1 deletion JSON/PY_CODE_TEMPLATE.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -47,7 +47,7 @@ def {model_function}(
nchain: int = 4,
ncore: int = 1,
nthin: int = 1,
inits: Union[str, Sequence[float]] = 'random',
inits: Union[str, Sequence[float]] = 'vb',
ind_pars: str = 'mean',
model_regressor: bool = False,
vb: bool = False,
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67 changes: 62 additions & 5 deletions Python/hbayesdm/base.py
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Expand Up @@ -21,7 +21,7 @@


class TaskModel(metaclass=ABCMeta):
"""HBayesDM TaskModel Base Class.
"""hBayesDM TaskModel Base Class.

The base class that is inherited by all hBayesDM task-models. Child classes
should implement (i.e. override) the abstract method: `_preprocess_func`.
Expand Down Expand Up @@ -165,7 +165,12 @@ def _run(self,
data_dict = self._preprocess_func(
raw_data, general_info, additional_args)
pars = self._prepare_pars(model_regressor, inc_postpred)
gen_init = self._prepare_gen_init(inits, general_info['n_subj'])

n_subj = general_info['n_subj']
if inits == 'vb':
gen_init = self._prepare_gen_init_vb(data_dict, n_subj)
else:
gen_init = self._prepare_gen_init(inits, n_subj)

model = self._get_model_full_name()
ncore = self._set_number_of_cores(ncore)
Expand Down Expand Up @@ -423,16 +428,68 @@ def _prepare_pars(self, model_regressor: bool, inc_postpred: bool) -> List:
pars += self.postpreds
return pars

def _prepare_gen_init_vb(self,
data_dict: Dict,
n_subj: int,
) -> Union[str, Callable]:
"""Prepare initial values for the parameters using Variational Bayesian
methods.

Parameters
----------
data_dict
Dict holding the data to pass to Stan.
n_subj
Total number of subjects in data.

Returns
-------
gen_init : Union[str, Callable]
A function that returns initial values for each parameter, based on
the variational Bayesian method.
"""
model = self._get_model_full_name()
sm = self._designate_stan_model(model)
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@JaeyeongYang
If you call self._designate_stan_model(model) within _prepare_gen_init_vb(), then _designate_stan_model() will end up being called twice within the entirety of _run(). Because (the Stanmodel caching and) user prompting occurs in _designate_stan_model(), the user will be prompted the line: 'Using cached StanModel:', cache_file a total of two times.


try:
fit = sm.vb(data=data_dict)
except Exception:
raise RuntimeError(
'Failed to get VB estimates for initial values. '
'Please re-run the code to try fitting model with VB.')
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@JaeyeongYang
Are there any plans to also fix the base.py code part for running hbayesdm with vb=True ? (Regarding #92)


len_param = len(self.parameters)
dict_vb = {
k: v
for k, v in zip(fit['mean_par_names'], fit['mean_pars'])
if k.startswith('sigma[') or '_pr[' in k
}

dict_init = {}
dict_init['mu_pr'] = \
[dict_vb['mu_pr[%d]' % (i + 1)] for i in range(len_param)]
dict_init['sigma'] = \
[dict_vb['sigma[%d]' % (i + 1)] for i in range(len_param)]
for p in self.parameters:
dict_init['%s_pr' % p] = \
[dict_vb['%s_pr[%d]' % (p, i + 1)] for i in range(n_subj)]

def gen_init():
return dict_init

return gen_init

def _prepare_gen_init(self,
inits: Union[str, Sequence[float]],
n_subj: int) -> Union[str, Callable]:
n_subj: int,
) -> Union[str, Callable]:
"""Prepare initial values for the parameters.

Parameters
----------
inits
User-defined inits. Can be the strings 'random' or 'fixed', or a
list of float values to use as initial values for the parameters.
User-defined inits. Can be the strings 'random' or 'fixed',
or a list of float values to use as initial values for parameters.
n_subj
Total number of subjects in data.

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2 changes: 1 addition & 1 deletion Python/hbayesdm/models/_bandit2arm_delta.py
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Expand Up @@ -54,7 +54,7 @@ def bandit2arm_delta(
nchain: int = 4,
ncore: int = 1,
nthin: int = 1,
inits: Union[str, Sequence[float]] = 'random',
inits: Union[str, Sequence[float]] = 'vb',
ind_pars: str = 'mean',
model_regressor: bool = False,
vb: bool = False,
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2 changes: 1 addition & 1 deletion Python/hbayesdm/models/_bandit4arm2_kalman_filter.py
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Expand Up @@ -62,7 +62,7 @@ def bandit4arm2_kalman_filter(
nchain: int = 4,
ncore: int = 1,
nthin: int = 1,
inits: Union[str, Sequence[float]] = 'random',
inits: Union[str, Sequence[float]] = 'vb',
ind_pars: str = 'mean',
model_regressor: bool = False,
vb: bool = False,
Expand Down
2 changes: 1 addition & 1 deletion Python/hbayesdm/models/_bandit4arm_2par_lapse.py
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Expand Up @@ -57,7 +57,7 @@ def bandit4arm_2par_lapse(
nchain: int = 4,
ncore: int = 1,
nthin: int = 1,
inits: Union[str, Sequence[float]] = 'random',
inits: Union[str, Sequence[float]] = 'vb',
ind_pars: str = 'mean',
model_regressor: bool = False,
vb: bool = False,
Expand Down
2 changes: 1 addition & 1 deletion Python/hbayesdm/models/_bandit4arm_4par.py
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Expand Up @@ -59,7 +59,7 @@ def bandit4arm_4par(
nchain: int = 4,
ncore: int = 1,
nthin: int = 1,
inits: Union[str, Sequence[float]] = 'random',
inits: Union[str, Sequence[float]] = 'vb',
ind_pars: str = 'mean',
model_regressor: bool = False,
vb: bool = False,
Expand Down
2 changes: 1 addition & 1 deletion Python/hbayesdm/models/_bandit4arm_lapse.py
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Expand Up @@ -61,7 +61,7 @@ def bandit4arm_lapse(
nchain: int = 4,
ncore: int = 1,
nthin: int = 1,
inits: Union[str, Sequence[float]] = 'random',
inits: Union[str, Sequence[float]] = 'vb',
ind_pars: str = 'mean',
model_regressor: bool = False,
vb: bool = False,
Expand Down
2 changes: 1 addition & 1 deletion Python/hbayesdm/models/_bandit4arm_lapse_decay.py
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Expand Up @@ -63,7 +63,7 @@ def bandit4arm_lapse_decay(
nchain: int = 4,
ncore: int = 1,
nthin: int = 1,
inits: Union[str, Sequence[float]] = 'random',
inits: Union[str, Sequence[float]] = 'vb',
ind_pars: str = 'mean',
model_regressor: bool = False,
vb: bool = False,
Expand Down
2 changes: 1 addition & 1 deletion Python/hbayesdm/models/_bandit4arm_singleA_lapse.py
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Expand Up @@ -59,7 +59,7 @@ def bandit4arm_singleA_lapse(
nchain: int = 4,
ncore: int = 1,
nthin: int = 1,
inits: Union[str, Sequence[float]] = 'random',
inits: Union[str, Sequence[float]] = 'vb',
ind_pars: str = 'mean',
model_regressor: bool = False,
vb: bool = False,
Expand Down
2 changes: 1 addition & 1 deletion Python/hbayesdm/models/_bart_par4.py
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Expand Up @@ -58,7 +58,7 @@ def bart_par4(
nchain: int = 4,
ncore: int = 1,
nthin: int = 1,
inits: Union[str, Sequence[float]] = 'random',
inits: Union[str, Sequence[float]] = 'vb',
ind_pars: str = 'mean',
model_regressor: bool = False,
vb: bool = False,
Expand Down
2 changes: 1 addition & 1 deletion Python/hbayesdm/models/_choiceRT_ddm.py
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Expand Up @@ -58,7 +58,7 @@ def choiceRT_ddm(
nchain: int = 4,
ncore: int = 1,
nthin: int = 1,
inits: Union[str, Sequence[float]] = 'random',
inits: Union[str, Sequence[float]] = 'vb',
ind_pars: str = 'mean',
model_regressor: bool = False,
vb: bool = False,
Expand Down
2 changes: 1 addition & 1 deletion Python/hbayesdm/models/_choiceRT_ddm_single.py
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Expand Up @@ -58,7 +58,7 @@ def choiceRT_ddm_single(
nchain: int = 4,
ncore: int = 1,
nthin: int = 1,
inits: Union[str, Sequence[float]] = 'random',
inits: Union[str, Sequence[float]] = 'vb',
ind_pars: str = 'mean',
model_regressor: bool = False,
vb: bool = False,
Expand Down
2 changes: 1 addition & 1 deletion Python/hbayesdm/models/_cra_exp.py
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Expand Up @@ -62,7 +62,7 @@ def cra_exp(
nchain: int = 4,
ncore: int = 1,
nthin: int = 1,
inits: Union[str, Sequence[float]] = 'random',
inits: Union[str, Sequence[float]] = 'vb',
ind_pars: str = 'mean',
model_regressor: bool = False,
vb: bool = False,
Expand Down
2 changes: 1 addition & 1 deletion Python/hbayesdm/models/_cra_linear.py
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Expand Up @@ -62,7 +62,7 @@ def cra_linear(
nchain: int = 4,
ncore: int = 1,
nthin: int = 1,
inits: Union[str, Sequence[float]] = 'random',
inits: Union[str, Sequence[float]] = 'vb',
ind_pars: str = 'mean',
model_regressor: bool = False,
vb: bool = False,
Expand Down
2 changes: 1 addition & 1 deletion Python/hbayesdm/models/_dbdm_prob_weight.py
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Expand Up @@ -63,7 +63,7 @@ def dbdm_prob_weight(
nchain: int = 4,
ncore: int = 1,
nthin: int = 1,
inits: Union[str, Sequence[float]] = 'random',
inits: Union[str, Sequence[float]] = 'vb',
ind_pars: str = 'mean',
model_regressor: bool = False,
vb: bool = False,
Expand Down
2 changes: 1 addition & 1 deletion Python/hbayesdm/models/_dd_cs.py
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Expand Up @@ -59,7 +59,7 @@ def dd_cs(
nchain: int = 4,
ncore: int = 1,
nthin: int = 1,
inits: Union[str, Sequence[float]] = 'random',
inits: Union[str, Sequence[float]] = 'vb',
ind_pars: str = 'mean',
model_regressor: bool = False,
vb: bool = False,
Expand Down
2 changes: 1 addition & 1 deletion Python/hbayesdm/models/_dd_cs_single.py
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Expand Up @@ -59,7 +59,7 @@ def dd_cs_single(
nchain: int = 4,
ncore: int = 1,
nthin: int = 1,
inits: Union[str, Sequence[float]] = 'random',
inits: Union[str, Sequence[float]] = 'vb',
ind_pars: str = 'mean',
model_regressor: bool = False,
vb: bool = False,
Expand Down
2 changes: 1 addition & 1 deletion Python/hbayesdm/models/_dd_exp.py
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Expand Up @@ -57,7 +57,7 @@ def dd_exp(
nchain: int = 4,
ncore: int = 1,
nthin: int = 1,
inits: Union[str, Sequence[float]] = 'random',
inits: Union[str, Sequence[float]] = 'vb',
ind_pars: str = 'mean',
model_regressor: bool = False,
vb: bool = False,
Expand Down
2 changes: 1 addition & 1 deletion Python/hbayesdm/models/_dd_hyperbolic.py
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Expand Up @@ -57,7 +57,7 @@ def dd_hyperbolic(
nchain: int = 4,
ncore: int = 1,
nthin: int = 1,
inits: Union[str, Sequence[float]] = 'random',
inits: Union[str, Sequence[float]] = 'vb',
ind_pars: str = 'mean',
model_regressor: bool = False,
vb: bool = False,
Expand Down
2 changes: 1 addition & 1 deletion Python/hbayesdm/models/_dd_hyperbolic_single.py
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Expand Up @@ -57,7 +57,7 @@ def dd_hyperbolic_single(
nchain: int = 4,
ncore: int = 1,
nthin: int = 1,
inits: Union[str, Sequence[float]] = 'random',
inits: Union[str, Sequence[float]] = 'vb',
ind_pars: str = 'mean',
model_regressor: bool = False,
vb: bool = False,
Expand Down
2 changes: 1 addition & 1 deletion Python/hbayesdm/models/_gng_m1.py
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Expand Up @@ -60,7 +60,7 @@ def gng_m1(
nchain: int = 4,
ncore: int = 1,
nthin: int = 1,
inits: Union[str, Sequence[float]] = 'random',
inits: Union[str, Sequence[float]] = 'vb',
ind_pars: str = 'mean',
model_regressor: bool = False,
vb: bool = False,
Expand Down
2 changes: 1 addition & 1 deletion Python/hbayesdm/models/_gng_m2.py
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Expand Up @@ -62,7 +62,7 @@ def gng_m2(
nchain: int = 4,
ncore: int = 1,
nthin: int = 1,
inits: Union[str, Sequence[float]] = 'random',
inits: Union[str, Sequence[float]] = 'vb',
ind_pars: str = 'mean',
model_regressor: bool = False,
vb: bool = False,
Expand Down
2 changes: 1 addition & 1 deletion Python/hbayesdm/models/_gng_m3.py
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Expand Up @@ -65,7 +65,7 @@ def gng_m3(
nchain: int = 4,
ncore: int = 1,
nthin: int = 1,
inits: Union[str, Sequence[float]] = 'random',
inits: Union[str, Sequence[float]] = 'vb',
ind_pars: str = 'mean',
model_regressor: bool = False,
vb: bool = False,
Expand Down
2 changes: 1 addition & 1 deletion Python/hbayesdm/models/_gng_m4.py
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Expand Up @@ -67,7 +67,7 @@ def gng_m4(
nchain: int = 4,
ncore: int = 1,
nthin: int = 1,
inits: Union[str, Sequence[float]] = 'random',
inits: Union[str, Sequence[float]] = 'vb',
ind_pars: str = 'mean',
model_regressor: bool = False,
vb: bool = False,
Expand Down
2 changes: 1 addition & 1 deletion Python/hbayesdm/models/_igt_orl.py
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Expand Up @@ -61,7 +61,7 @@ def igt_orl(
nchain: int = 4,
ncore: int = 1,
nthin: int = 1,
inits: Union[str, Sequence[float]] = 'random',
inits: Union[str, Sequence[float]] = 'vb',
ind_pars: str = 'mean',
model_regressor: bool = False,
vb: bool = False,
Expand Down
2 changes: 1 addition & 1 deletion Python/hbayesdm/models/_igt_pvl_decay.py
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Expand Up @@ -59,7 +59,7 @@ def igt_pvl_decay(
nchain: int = 4,
ncore: int = 1,
nthin: int = 1,
inits: Union[str, Sequence[float]] = 'random',
inits: Union[str, Sequence[float]] = 'vb',
ind_pars: str = 'mean',
model_regressor: bool = False,
vb: bool = False,
Expand Down
2 changes: 1 addition & 1 deletion Python/hbayesdm/models/_igt_pvl_delta.py
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Expand Up @@ -59,7 +59,7 @@ def igt_pvl_delta(
nchain: int = 4,
ncore: int = 1,
nthin: int = 1,
inits: Union[str, Sequence[float]] = 'random',
inits: Union[str, Sequence[float]] = 'vb',
ind_pars: str = 'mean',
model_regressor: bool = False,
vb: bool = False,
Expand Down
2 changes: 1 addition & 1 deletion Python/hbayesdm/models/_igt_vpp.py
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Expand Up @@ -67,7 +67,7 @@ def igt_vpp(
nchain: int = 4,
ncore: int = 1,
nthin: int = 1,
inits: Union[str, Sequence[float]] = 'random',
inits: Union[str, Sequence[float]] = 'vb',
ind_pars: str = 'mean',
model_regressor: bool = False,
vb: bool = False,
Expand Down
2 changes: 1 addition & 1 deletion Python/hbayesdm/models/_peer_ocu.py
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Expand Up @@ -61,7 +61,7 @@ def peer_ocu(
nchain: int = 4,
ncore: int = 1,
nthin: int = 1,
inits: Union[str, Sequence[float]] = 'random',
inits: Union[str, Sequence[float]] = 'vb',
ind_pars: str = 'mean',
model_regressor: bool = False,
vb: bool = False,
Expand Down
2 changes: 1 addition & 1 deletion Python/hbayesdm/models/_prl_ewa.py
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Expand Up @@ -59,7 +59,7 @@ def prl_ewa(
nchain: int = 4,
ncore: int = 1,
nthin: int = 1,
inits: Union[str, Sequence[float]] = 'random',
inits: Union[str, Sequence[float]] = 'vb',
ind_pars: str = 'mean',
model_regressor: bool = False,
vb: bool = False,
Expand Down
2 changes: 1 addition & 1 deletion Python/hbayesdm/models/_prl_fictitious.py
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Expand Up @@ -60,7 +60,7 @@ def prl_fictitious(
nchain: int = 4,
ncore: int = 1,
nthin: int = 1,
inits: Union[str, Sequence[float]] = 'random',
inits: Union[str, Sequence[float]] = 'vb',
ind_pars: str = 'mean',
model_regressor: bool = False,
vb: bool = False,
Expand Down
2 changes: 1 addition & 1 deletion Python/hbayesdm/models/_prl_fictitious_multipleB.py
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Expand Up @@ -61,7 +61,7 @@ def prl_fictitious_multipleB(
nchain: int = 4,
ncore: int = 1,
nthin: int = 1,
inits: Union[str, Sequence[float]] = 'random',
inits: Union[str, Sequence[float]] = 'vb',
ind_pars: str = 'mean',
model_regressor: bool = False,
vb: bool = False,
Expand Down
2 changes: 1 addition & 1 deletion Python/hbayesdm/models/_prl_fictitious_rp.py
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Expand Up @@ -62,7 +62,7 @@ def prl_fictitious_rp(
nchain: int = 4,
ncore: int = 1,
nthin: int = 1,
inits: Union[str, Sequence[float]] = 'random',
inits: Union[str, Sequence[float]] = 'vb',
ind_pars: str = 'mean',
model_regressor: bool = False,
vb: bool = False,
Expand Down
2 changes: 1 addition & 1 deletion Python/hbayesdm/models/_prl_fictitious_rp_woa.py
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Expand Up @@ -60,7 +60,7 @@ def prl_fictitious_rp_woa(
nchain: int = 4,
ncore: int = 1,
nthin: int = 1,
inits: Union[str, Sequence[float]] = 'random',
inits: Union[str, Sequence[float]] = 'vb',
ind_pars: str = 'mean',
model_regressor: bool = False,
vb: bool = False,
Expand Down
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