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Commit 5e31e5f6 authored by Tiago Peixoto's avatar Tiago Peixoto
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uncertain_blockmodel.py: simplify super() usage

parent f4b7c076
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src/graph_tool/inference/uncertain_blockmodel.py
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......@@ -392,11 +392,8 @@ class UncertainBlockState(UncertainBaseState):
def __init__(self, g, q, q_default=0., aE=numpy.nan, nested=True, state_args={},
bstate=None, self_loops=False, **kwargs):
super(UncertainBlockState, self).__init__(g, nested=nested,
state_args=state_args,
bstate=bstate,
self_loops=self_loops,
**kwargs)
super().__init__(g, nested=nested, state_args=state_args, bstate=bstate,
self_loops=self_loops, **kwargs)
self._q = q
self._q_default = q_default
......@@ -423,7 +420,7 @@ class UncertainBlockState(UncertainBaseState):
self._state = libinference.make_uncertain_state(self.bstate._state,
self)
def __getstate__(self):
state = super(UncertainBlockState, self).__getstate__()
state = super().__getstate__()
return dict(state, q=self._q, q_default=self._q_default,
aE=self.aE)
......@@ -479,11 +476,8 @@ class LatentMultigraphBlockState(UncertainBaseState):
def __init__(self, g, aE=numpy.nan, nested=True, state_args={},
bstate=None, self_loops=False, **kwargs):
super(LatentMultigraphBlockState, self).__init__(g, nested=nested,
state_args=state_args,
bstate=bstate,
self_loops=self_loops,
**kwargs)
super().__init__(g, nested=nested, state_args=state_args, bstate=bstate,
self_loops=self_loops, **kwargs)
self.q = self.g.new_ep("double", val=numpy.inf)
self.q_default = -numpy.inf
......@@ -498,7 +492,7 @@ class LatentMultigraphBlockState(UncertainBaseState):
self._state = libinference.make_uncertain_state(self.bstate._state,
self)
def __getstate__(self):
state = super(LatentMultigraphBlockState, self).__getstate__()
state = super().__getstate__()
return dict(state, aE=self.aE)
def __setstate__(self, state):
......@@ -576,9 +570,8 @@ class MeasuredBlockState(UncertainBaseState):
aE=numpy.nan, nested=True, state_args={}, bstate=None,
self_loops=False, **kwargs):
super(MeasuredBlockState, self).__init__(g, nested=nested,
state_args=state_args,
bstate=bstate, **kwargs)
super().__init__(g, nested=nested, state_args=state_args, bstate=bstate,
**kwargs)
self.aE = aE
if numpy.isnan(aE):
......@@ -599,7 +592,7 @@ class MeasuredBlockState(UncertainBaseState):
self)
def __getstate__(self):
state = super(MeasuredBlockState, self).__getstate__()
state = super().__getstate__()
return dict(state, n=self.n, x=self.x, n_default=self.n_default,
x_default=self.x_default,
fn_params=dict(alpha=self.alpha, beta=self.beta),
......@@ -695,9 +688,8 @@ class MixedMeasuredBlockState(UncertainBaseState):
aE=numpy.nan, nested=True, state_args={}, bstate=None,
self_loops=False, **kwargs):
super(MixedMeasuredBlockState, self).__init__(g, nested=nested,
state_args=state_args,
bstate=bstate, **kwargs)
super().__init__(g, nested=nested, state_args=state_args, bstate=bstate,
**kwargs)
self.aE = aE
if numpy.isnan(aE):
self.E_prior = False
......@@ -749,7 +741,7 @@ class MixedMeasuredBlockState(UncertainBaseState):
self.sync_q()
def __getstate__(self):
state = super(MixedMeasuredBlockState, self).__getstate__()
state = super().__getstate__()
return dict(state, n=self.n, x=self.x, n_default=self.n_default,
x_default=self.x_default,
fn_params=dict(alpha=self.alpha, beta=self.beta),
......@@ -784,10 +776,8 @@ class MixedMeasuredBlockState(UncertainBaseState):
if ``multiflip=True``.
"""
return super(MixedMeasuredBlockState, self).mcmc_sweep(r=r,
multiflip=multiflip,
h=h, hstep=hstep,
**kwargs)
return super().mcmc_sweep(r=r, multiflip=multiflip, h=h, hstep=hstep,
**kwargs)
def _algo_sweep(self, algo, r=.5, h=.1, hstep=1, niter=1, **kwargs):
if numpy.random.random() < h:
......@@ -821,9 +811,7 @@ class MixedMeasuredBlockState(UncertainBaseState):
self.set_hparams(*hs)
return (dS, nt, nm)
else:
return super(MixedMeasuredBlockState, self)._algo_sweep(algo, r,
niter=niter,
**kwargs)
return super()._algo_sweep(algo, r, niter=niter, **kwargs)
def _mcmc_sweep(self, mcmc_state):
return libinference.mcmc_uncertain_sweep(mcmc_state,
......@@ -838,10 +826,8 @@ class DynamicsBlockStateBase(UncertainBaseState):
the stochastic block model as a prior. This class is not meant to be
instantiated directly, only indirectly via one of its subclasses."""
super(DynamicsBlockStateBase, self).__init__(g, nested=nested,
state_args=state_args,
bstate=bstate,
self_loops=self_loops)
super().__init__(g, nested=nested, state_args=state_args, bstate=bstate,
self_loops=self_loops)
self.s = [g.own_property(x) for x in s]
self.t = [g.own_property(x) for x in t]
if x is None:
......@@ -874,7 +860,7 @@ class DynamicsBlockStateBase(UncertainBaseState):
self._state.set_params(self.params)
def __getstate__(self):
state = super(DynamicsBlockState, self).__getstate__()
state = super().__getstate__()
return dict(state, s=self.s, t=self.t, x=self.x, aE=self.aE,
**self.params)
......@@ -1108,16 +1094,11 @@ class EpidemicsBlockState(DynamicsBlockStateBase):
else:
x = u.new_ep("double", val=beta)
x.fa = log1p(-x.fa)
super(EpidemicsBlockState, self).__init__(g, s=s, t=t,
global_beta=global_beta,
active=active,
x=x, r=r, r_v=r_v,
exposed=exposed, aE=aE,
nested=nested,
state_args=state_args,
bstate=bstate,
self_loops=self_loops,
**kwargs)
super().__init__(g, s=s, t=t, global_beta=global_beta, active=active,
x=x, r=r, r_v=r_v, exposed=exposed, aE=aE,
nested=nested, state_args=state_args, bstate=bstate,
self_loops=self_loops, **kwargs)
def _make_state(self):
return libinference.make_epidemics_state(self.bstate._state, self)
......@@ -1167,11 +1148,8 @@ class EpidemicsBlockState(DynamicsBlockStateBase):
"""
return super(EpidemicsBlockState, self).mcmc_sweep(r=r, p=p, pstep=p,
h=h, hstep=hstep,
xstep=xstep,
multiflip=multiflip,
**kwargs)
return super().mcmc_sweep(r=r, p=p, pstep=p, h=h, hstep=hstep,
xstep=xstep, multiflip=multiflip, **kwargs)
def _algo_sweep(self, algo, r=.5, p=.1, pstep=.1, h=.1, hstep=1,
xstep=.1, niter=1, **kwargs):
......@@ -1214,12 +1192,8 @@ class EpidemicsBlockState(DynamicsBlockStateBase):
xdefault = log1p(-beta)
else:
xdefault = 0
return super(EpidemicsBlockState, self)._algo_sweep(algo, r,
xlog=True,
xstep=xstep,
xdefault=xdefault,
niter=niter,
**kwargs)
return super()._algo_sweep(algo, r, xlog=True, xstep=xstep,
xdefault=xdefault, niter=niter, **kwargs)
def _mcmc_sweep(self, mcmc_state):
return libinference.mcmc_epidemics_sweep(mcmc_state, self._state,
......@@ -1314,12 +1288,11 @@ class IsingBaseBlockState(DynamicsBlockStateBase):
x = u.new_ep("double", 1)
elif x is None:
x = u.new_ep("double", numpy.random.random(g.num_edges()) - 1/2)
super(IsingBaseBlockState, self).__init__(g, s=s, t=t, beta=beta, x=x,
aE=aE, nested=nested,
state_args=state_args,
bstate=bstate,
self_loops=self_loops, h=h,
has_zero=has_zero, **kwargs)
super().__init__(g, s=s, t=t, beta=beta, x=x, aE=aE, nested=nested,
state_args=state_args, bstate=bstate,
self_loops=self_loops, h=h, has_zero=has_zero,
**kwargs)
def __setstate__(self, state):
beta = state.pop("beta", None)
if "x" not in state:
......@@ -1351,11 +1324,8 @@ class IsingBaseBlockState(DynamicsBlockStateBase):
if ``multiflip=True``.
"""
return super(IsingBaseBlockState, self).mcmc_sweep(r=r, p=p, pstep=p,
h=h, hstep=hstep,
xstep=xstep,
multiflip=multiflip,
**kwargs)
return super().mcmc_sweep(r=r, p=p, pstep=p, h=h, hstep=hstep,
xstep=xstep, multiflip=multiflip, **kwargs)
def _algo_sweep(self, algo, r=.5, p=.1, pstep=1, h=.5, hstep=1, niter=1,
xstep=1, **kwargs):
......@@ -1380,12 +1350,8 @@ class IsingBaseBlockState(DynamicsBlockStateBase):
nm += ret[2]
return (dS, nt, nm)
else:
return super(IsingBaseBlockState, self)._algo_sweep(algo, r,
xlog=False,
xstep=xstep,
xdefault=1,
niter=niter,
**kwargs)
return super()._algo_sweep(algo, r, xlog=False, xstep=xstep,
xdefault=1, niter=niter, **kwargs)
class IsingGlauberBlockState(IsingBaseBlockState):
def __init__(self, *args, **kwargs):
......@@ -1395,7 +1361,7 @@ class IsingGlauberBlockState(IsingBaseBlockState):
See documentation for :class:`IsingBaseBlockState` for details.
"""
super(IsingGlauberBlockState, self).__init__(*args, **kwargs)
super().__init__(*args, **kwargs)
def _make_state(self):
return libinference.make_ising_glauber_state(self.bstate._state, self)
......@@ -1417,7 +1383,7 @@ class CIsingGlauberBlockState(IsingBaseBlockState):
that in this case the ``s`` parameter should contain property maps of
type ``vector<double>``, with values in the range :math:`[-1,1]`.
"""
super(CIsingGlauberBlockState, self).__init__(*args, **kwargs)
super().__init__(*args, **kwargs)
def _make_state(self):
return libinference.make_cising_glauber_state(self.bstate._state, self)
......@@ -1439,7 +1405,7 @@ class PseudoIsingBlockState(IsingBaseBlockState):
that in this model "time-series" should be interpreted as a set of
uncorrelated samples, not a temporal sequence.
"""
super(PseudoIsingBlockState, self).__init__(*args, **kwargs)
super().__init__(*args, **kwargs)
def _make_state(self):
return libinference.make_pseudo_ising_state(self.bstate._state, self)
......@@ -1463,7 +1429,7 @@ class PseudoCIsingBlockState(IsingBaseBlockState):
parameter should contain property maps of type ``vector<double>``, with
values in the range :math:`[-1,1]`.
"""
super(PseudoCIsingBlockState, self).__init__(*args, **kwargs)
super().__init__(*args, **kwargs)
def _make_state(self):
return libinference.make_pseudo_cising_state(self.bstate._state, self)
......
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