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Fix docstring and apply black formatter to mlda #4162

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Oct 8, 2020
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Fix docstring and apply black formatter to mlda #4162

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51 changes: 22 additions & 29 deletions pymc3/step_methods/mlda.py
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Original file line number Diff line number Diff line change
Expand Up @@ -106,21 +106,21 @@ class MLDA(ArrayStepShared):
... datum = 1
...
... with pm.Model() as coarse_model:
... x = Normal("x", mu=0, sigma=10)
... y = Normal("y", mu=x, sigma=1, observed=datum - 0.1)
... x = pm.Normal("x", mu=0, sigma=10)
... y = pm.Normal("y", mu=x, sigma=1, observed=datum - 0.1)
...
... with pm.Model():
... x = Normal("x", mu=0, sigma=10)
... y = Normal("y", mu=x, sigma=1, observed=datum)
... step_method = pm.MLDA(coarse_models=[coarse_model]
... x = pm.Normal("x", mu=0, sigma=10)
... y = pm.Normal("y", mu=x, sigma=1, observed=datum)
... step_method = pm.MLDA(coarse_models=[coarse_model],
... subsampling_rates=5)
... trace = pm.sample(ndraws=500, chains=2,
... trace = pm.sample(500, chains=2,
... tune=100, step=step_method,
... random_seed=123)
...
... pm.summary(trace)
mean sd hpd_3% hpd_97%
x 1.011 0.975 -0.925 2.824
... pm.summary(trace, kind="stats")
mean sd hdi_3% hdi_97%
x 0.99 0.987 -0.734 2.992

References
----------
Expand Down Expand Up @@ -161,7 +161,7 @@ def __init__(
mode: Optional = None,
subsampling_rates: List[int] = 5,
base_blocked: bool = False,
**kwargs
**kwargs,
) -> None:

warnings.warn(
Expand All @@ -174,9 +174,7 @@ def __init__(
# assign internal state
self.coarse_models = coarse_models
if not isinstance(coarse_models, list):
raise ValueError(
"MLDA step method cannot use coarse_models if it is not a list"
)
raise ValueError("MLDA step method cannot use coarse_models if it is not a list")
if len(self.coarse_models) == 0:
raise ValueError(
"MLDA step method was given an empty "
Expand Down Expand Up @@ -233,9 +231,7 @@ def __init__(
if self.num_levels == 2:
with self.next_model:
# make sure the correct variables are selected from next_model
vars_next = [
var for var in self.next_model.vars if var.name in self.var_names
]
vars_next = [var for var in self.next_model.vars if var.name in self.var_names]
# MetropolisMLDA sampler in base level (level=0), targeting self.next_model
self.next_step_method = pm.MetropolisMLDA(
vars=vars_next,
Expand All @@ -253,9 +249,7 @@ def __init__(
next_subsampling_rates = self.subsampling_rates[:-1]
with self.next_model:
# make sure the correct variables are selected from next_model
vars_next = [
var for var in self.next_model.vars if var.name in self.var_names
]
vars_next = [var for var in self.next_model.vars if var.name in self.var_names]
# MLDA sampler in some intermediate level, targeting self.next_model
self.next_step_method = pm.MLDA(
vars=vars_next,
Expand Down Expand Up @@ -335,9 +329,7 @@ def astep(self, q0):
self.base_scaling_stats = {"base_scaling": np.array(scaling_list)}
elif not isinstance(self.next_step_method, MLDA):
# next method is any block sampler
self.base_scaling_stats = {
"base_scaling": np.array(self.next_step_method.scaling)
}
self.base_scaling_stats = {"base_scaling": np.array(self.next_step_method.scaling)}
else:
# next method is MLDA - propagate dict from lower levels
self.base_scaling_stats = self.next_step_method.base_scaling_stats
Expand Down Expand Up @@ -366,19 +358,20 @@ class RecursiveDAProposal(Proposal):
each of which is used to propose samples to the chain above.
"""

def __init__(self,
next_step_method: Union[MLDA, Metropolis, CompoundStep],
next_model: Model,
tune: bool,
subsampling_rate: int) -> None:
def __init__(
self,
next_step_method: Union[MLDA, Metropolis, CompoundStep],
next_model: Model,
tune: bool,
subsampling_rate: int,
) -> None:

self.next_step_method = next_step_method
self.next_model = next_model
self.tune = tune
self.subsampling_rate = subsampling_rate

def __call__(self,
q0_dict: dict) -> dict:
def __call__(self, q0_dict: dict) -> dict:
"""Returns proposed sample given the current sample
in dictionary form (q0_dict).
"""
Expand Down