def test_conv():
"""Test multichannel convolution with bypass"""
n_components = 1
seed = 42
L = 20
channels = 3
bypass = 1
svi = False
n_inputs = channels * L**2 + bypass
nn = NeuralNet(n_components=n_components,
n_filters=[8, 8],
n_hiddens=[10],
n_inputs=n_inputs,
input_shape=(channels, L, L),
n_bypass=1,
n_outputs=1,
seed=seed,
svi=svi)
eval_lprobs = theano.function([nn.params, nn.stats], nn.lprobs)
conv_sizes = [
lasagne.layers.get_output_shape(nn.layer['conv_' + str(i + 1)])
for i in range(2)
]
res = eval_lprobs(np.array([[1.], [2.]], dtype=dtype),
np.random.normal(size=(2, n_inputs)).astype(dtype))
mog = nn.get_mog(np.random.normal(size=(1, n_inputs)).astype(dtype))
def test_lprobs():
n_components = 2
seed = 42
svi = False
nn = NeuralNet(n_components=n_components,
n_hiddens=[10],
n_inputs=1,
n_outputs=1,
seed=seed,
svi=svi)
eval_lprobs = theano.function([nn.params, nn.stats], nn.lprobs)
res = eval_lprobs(np.array([[1.], [2.]], dtype=dtype),
np.array([[1.], [2.]], dtype=dtype))
mog = nn.get_mog(np.array([[1.]], dtype=dtype))
def test_diag_precision_bounds():
n_components = 2
seed = 42
svi = False
min_precisions = np.array([0.1, 2.0, 15.0])
nn = NeuralNet(n_components=n_components,
n_hiddens=[10],
n_inputs=3,
n_outputs=3,
seed=seed,
svi=svi,
min_precisions=None)
nn_bounded = NeuralNet(n_components=n_components,
n_hiddens=[10],
n_inputs=3,
n_outputs=3,
seed=seed,
svi=svi,
min_precisions=min_precisions)
mog_bounded = nn_bounded.get_mog(np.array(np.ones((1, 3)), dtype=dtype))
for x in mog_bounded.xs:
assert np.allclose(0.0, np.maximum(0.0, min_precisions - np.diag(x.P)))
def test_conv():
n_components = 1
seed = 42
svi = False
nn = NeuralNet(n_components=n_components,
n_filters=[8, 8],
n_hiddens=[10],
n_inputs=(1, 20, 20),
n_outputs=1,
seed=seed,
svi=svi)
eval_lprobs = theano.function([nn.params, nn.stats], nn.lprobs)
conv_sizes = [
lasagne.layers.get_output_shape(nn.layer['conv_' + str(i + 1)])
for i in range(2)
]
res = eval_lprobs(np.array([[1.], [2.]], dtype=dtype),
np.random.normal(size=(2, 1, 20, 20)).astype(dtype))
mog = nn.get_mog(np.ones((1, 1, 20, 20), dtype=dtype))
class BaseInference(metaclass=ABCMetaDoc):
def __init__(self, generator, prior_norm=True, pilot_samples=100,
seed=None, verbose=True, **kwargs):
"""Abstract base class for inference algorithms
Inference algorithms must at least implement abstract methods of this
class.
Parameters
----------
generator : generator instance
Generator instance
prior_norm : bool
If set to True, will z-transform params based on mean/std of prior
pilot_samples : None or int
If an integer is provided, a pilot run with the given number of
samples is run. The mean and std of the summary statistics of the
pilot samples will be subsequently used to z-transform summary
statistics.
seed : int or None
If provided, random number generator will be seeded
kwargs : additional keyword arguments
Additional arguments used when creating the NeuralNet instance
Attributes
----------
observables : dict
Dictionary containing theano variables that can be monitored while
training the neural network.
"""
self.seed = seed
if seed is not None:
self.rng = np.random.RandomState(seed=seed)
else:
self.rng = np.random.RandomState()
self.verbose = verbose
# bind generator, reset proposal attribute
self.generator = generator
self.generator.proposal = None
# generate a sample to get input and output dimensions
params, stats = generator.gen(1, skip_feedback=True, verbose=False)
kwargs.update({'n_inputs': stats.shape[1:],
'n_outputs': params.shape[1],
'seed': self.gen_newseed()})
self.network = NeuralNet(**kwargs)
self.svi = self.network.svi
# parameters for z-transform of params
if prior_norm:
# z-transform for params based on prior
self.params_mean = self.generator.prior.mean
self.params_std = self.generator.prior.std
else:
# parameters are set such that z-transform has no effect
self.params_mean = np.zeros((params.shape[1],))
self.params_std = np.ones((params.shape[1],))
# parameters for z-transform for stats
if pilot_samples is not None and pilot_samples != 0:
# determine via pilot run
self.pilot_run(pilot_samples)
else:
# parameters are set such that z-transform has no effect
self.stats_mean = np.zeros((stats.shape[1],))
self.stats_std = np.ones((stats.shape[1],))
# observables contains vars that can be monitored during training
self.compile_observables()
@abc.abstractmethod
def loss(self):
pass
@abc.abstractmethod
def run(self):
pass
def gen(self, n_samples, n_reps=1, prior_mixin=0, verbose=None):
"""Generate from generator and z-transform
Parameters
----------
n_samples : int
Number of samples to generate
n_reps : int
Number of repeats per parameter
verbose : None or bool or str
If None is passed, will default to self.verbose
"""
verbose = self.verbose if verbose is None else verbose
params, stats = self.generator.gen(n_samples, prior_mixin=prior_mixin, verbose=verbose)
# z-transform params and stats
params = (params - self.params_mean) / self.params_std
stats = (stats - self.stats_mean) / self.stats_std
return params, stats
def gen_newseed(self):
"""Generates a new random seed"""
if self.seed is None:
return None
else:
return self.rng.randint(0, 2**31)
def pilot_run(self, n_samples):
"""Pilot run in order to find parameters for z-scoring stats
"""
verbose = '(pilot run) ' if self.verbose else False
params, stats = self.generator.gen(n_samples, verbose=verbose)
self.stats_mean = np.nanmean(stats, axis=0)
self.stats_std = np.nanstd(stats, axis=0)
def predict(self, x, deterministic=True):
"""Predict posterior given x
Parameters
----------
x : array
Stats for which to compute the posterior
deterministic : bool
if True, mean weights are used for Bayesian network
"""
x_zt = (x - self.stats_mean) / self.stats_std
posterior = self.network.get_mog(x_zt, deterministic=deterministic)
return posterior.ztrans_inv(self.params_mean, self.params_std)
def compile_observables(self):
"""Creates observables dict"""
self.observables = {}
self.observables['loss.lprobs'] = self.network.lprobs
for p in self.network.aps:
self.observables[str(p)] = p
def monitor_dict_from_names(self, monitor=None):
"""Generate monitor dict from list of variable names"""
if monitor is not None:
observe = {}
if isinstance(monitor, str):
monitor = [monitor]
for m in monitor:
if m in self.observables:
observe[m] = self.observables[m]
else:
observe = None
return observe
class BaseInference(metaclass=ABCMetaDoc):
def __init__(self, generator,
prior_norm=True, init_norm=False,
pilot_samples=100,
seed=None, verbose=True, **kwargs):
"""Abstract base class for inference algorithms
Inference algorithms must at least implement abstract methods of this
class.
Parameters
----------
generator : generator instance
Generator instance
prior_norm : bool
If set to True, will z-transform params based on mean/std of prior
pilot_samples : None or int or tuple
If an integer is provided, a pilot run with the given number of
samples is run. The mean and std of the summary statistics of the
pilot samples will be subsequently used to z-transform summary
statistics.
If an tuple of the form (params, stats) is provided, these will be
used directly as samples from the prior.
seed : int or None
If provided, random number generator will be seeded
kwargs : additional keyword arguments
Additional arguments used when creating the NeuralNet instance
Attributes
----------
observables : dict
Dictionary containing theano variables that can be monitored while
training the neural network.
"""
self.seed = seed
if seed is not None:
self.rng = np.random.RandomState(seed=seed)
else:
self.rng = np.random.RandomState()
self.verbose = verbose
# bind generator, reset proposal attribute
self.generator = generator
self.generator.proposal = None
# get input and output dimensions
if type(pilot_samples) is tuple:
params, stats = pilot_samples[0], pilot_samples[1]
else:
params, stats = generator.gen(1, skip_feedback=True, verbose=False)
assert stats.ndim == 2, "invalid summary stats"
kwargs.update({'n_inputs': stats.shape[1],
'n_outputs': params.shape[1],
'seed': self.gen_newseed()})
self.kwargs = kwargs
# optional: z-transform output for obs (also re-centres x onto obs!)
self.init_norm = init_norm
if 'n_components' in kwargs.keys() and kwargs['n_components'] > 1:
self.init_fcv = 0.8
else:
self.init_fcv = 0.0
# parameters for z-transform of params
if prior_norm:
# z-transform for params based on prior
self.params_mean = self.generator.prior.mean
self.params_std = self.generator.prior.std
assert not np.any(np.isnan(self.params_mean)) and \
not np.any(np.isnan(self.params_std)) and \
self.params_mean is not None and \
self.params_std is not None
else:
# parameters are set such that z-transform has no effect
self.params_mean = np.zeros((params.shape[1],))
self.params_std = np.ones((params.shape[1],))
self.pilot_run(pilot_samples, stats.shape[1])
self.reinit_network() # init network, then update self.kwargs['seed']
# observables contains vars that can be monitored during training
self.compile_observables()
self.round = 0
@abc.abstractmethod
def loss(self):
pass
@abc.abstractmethod
def run(self):
pass
def run_repeated(self, n_repeats=10, n_NN_inits_per_repeat=1,
callback=None, **kwargs):
"""Repeatedly run the method and collect results. Optionally, carry out
several runs with the same initial generator RNG state but different
neural network initializations.
parameters
----------
n_repeats : int
Number of times to run the algorithm
n_NN_inits : int
Number of times to
callback: function
callback function that will be called after each run. It should
take 4 inputs: callback(log, train_data, posterior, self)
kwargs : additional keyword arguments
Additional arguments that will be passed to the run() method
"""
posteriors, outputs, repeat_index = [], [], []
for r in range(n_repeats):
if n_NN_inits_per_repeat > 1:
generator_seed = self.gen_newseed()
for i in range(n_NN_inits_per_repeat):
self.reset()
if n_NN_inits_per_repeat > 1:
self.generator.reseed(generator_seed)
log, train_data, posterior = self.run(**kwargs)
if callback is not None:
outputs.append(callback(log, train_data, posterior, self))
else:
outputs.append(None)
posteriors.append(posterior)
repeat_index.append(r)
return posteriors, outputs, repeat_index
def reinit_network(self):
"""Reinitializes the network instance (re-setting the weights!)
"""
self.network = NeuralNet(**self.kwargs)
self.svi = self.network.svi if 'svi' in dir(self.network) else False
"""update self.kwargs['seed'] so that reinitializing the network gives a
different result each time unless we reseed the inference method"""
self.kwargs['seed'] = self.gen_newseed()
self.norm_init()
def centre_on_obs(self):
""" Centres first-layer input onto observed summary statistics
Ensures x' = x - xo, i.e. first-layer input x' = 0 for x = xo.
"""
self.stats_mean = self.obs.copy()
def remove_hidden_biases(self):
""" Resets all bias weights in hidden layers to zero.
"""
def idx_hiddens(x):
return x.name[0] == 'h'
for b in filter(idx_hiddens, self.network.mps_bp):
b.set_value(np.zeros_like(b.get_value()))
def conditional_norm(self, fcv=0.8, tmu=None, tSig=None, h=None):
""" Normalizes current network output at observed summary statistics
Parameters
----------
fcv : float
Fraction of total that comes from uncertainty over components, i.e.
Var[th] = E[Var[th|z]] + Var[E[th|z]]
= (1-fcv) + fcv = 1
tmu: array
Target mean.
tSig: array
Target covariance.
"""
# avoid CDELFI.predict() attempt to analytically correct for proposal
print('obs', self.obs.shape)
print('mean', self.stats_mean.shape)
print('std', self.stats_std.shape)
obz = (self.obs - self.stats_mean) / self.stats_std
posterior = self.network.get_mog(obz.reshape(self.obs.shape),
deterministic=True)
mog = posterior.ztrans_inv(self.params_mean, self.params_std)
assert np.all(np.diff(mog.a)==0.) # assumes uniform alpha
n_dim = self.kwargs['n_outputs']
triu_mask = np.triu(np.ones([n_dim, n_dim], dtype=dtype), 1)
diag_mask = np.eye(n_dim, dtype=dtype)
# compute MoG mean mu, Sig = E[Var[th|z]] and C = Var[E[th|z]]
mu, Sig = np.zeros_like(mog.xs[0].m), np.zeros_like(mog.xs[0].S)
for i in range(self.network.n_components):
Sig += mog.a[i] * mog.xs[i].S
mu += mog.a[i] * mog.xs[i].m
C = np.zeros_like(Sig)
for i in range(self.network.n_components):
dmu = mog.xs[i].m - mu if self.network.n_components > 1 \
else mog.xs[i].m
C += mog.a[i] * np.outer(dmu, dmu)
# if not provided, target zero-mean unit variance (as for prior_norm=True)
tmu = np.zeros_like(mog.xs[0].m) if tmu is None else tmu
tSig = np.eye(mog.xs[0].m.size) if tSig is None else tSig
# compute normalizers (we only z-score, don't whiten!)
Z1inv = np.sqrt((1.-fcv) / np.diag(Sig) * np.diag(tSig)).reshape(-1)
Z2inv = np.sqrt( fcv / np.diag( C ) * np.diag(tSig)).reshape(-1)
# first we need the center of means
def idx_MoG(x):
return x.name[:5]=='means'
mu_ = np.zeros_like(mog.xs[0].m)
for w, b in zip(filter(idx_MoG, self.network.mps_wp),
filter(idx_MoG, self.network.mps_bp)):
h = np.zeros(w.get_value().shape[0]) if h is None else h
mu_ += h.dot(w.get_value()) + b.get_value()
mu_ /= self.network.n_components
# center and normalize means
# mu = Z2inv * (Wh + b - mu_) + tmu
# = Wh + (Z2inv * (b - mu_ + Wh) - Wh + tum)
for w, b in zip(filter(idx_MoG, self.network.mps_wp),
filter(idx_MoG, self.network.mps_bp)):
Wh = h.dot(w.get_value())
b.set_value(Z2inv * (Wh + b.get_value() - mu_) - Wh + tmu)
# normalize covariances
def idx_MoG(x):
return x.name[:10]=='precisions'
# Sig^-0.5 = diag_mask * (exp(Wh+b)/exp(log(Z1)) + triu_mask * (Wh+b)*Z1
# = diag_mask * exp(Wh+ (b-log(Z1)) + triu_mask * (Wh+((b+Wh)*Z1-Wh))
for w, b in zip(filter(idx_MoG, self.network.mps_wp),
filter(idx_MoG, self.network.mps_bp)):
Wh = h.dot(w.get_value()).reshape(n_dim,n_dim)
b_ = b.get_value().copy().reshape(n_dim,n_dim)
val = diag_mask * (b_ - np.diag(np.log(Z1inv))) + triu_mask * ((b_+Wh).dot(np.diag(1./Z1inv))- Wh )
b.set_value(val.flatten())
def norm_init(self):
if self.init_norm and self.network.density == 'mog':
print('standardizing network initialization')
if self.network.n_components > 1:
self.standardize_init(fcv = self.init_fcv)
else:
self.standardize_init(fcv = 0.)
def standardize_init(self, fcv = 0.8):
""" Standardizes the network initialization on obs
Ensures output distributions for xo have mean zero and unit variance.
Alters hidden layers to propagates x=xo as zero to the last layer, and
alters the MoG layers to produce the desired output distribution.
"""
assert isinstance(self.network, NeuralNet)
# ensure x' = x - xo
self.centre_on_obs()
# ensure x' = 0 stays zero up to MoG layer (setting biases to zero)
self.remove_hidden_biases()
# ensure MoG returns standardized output on x' = 0
self.conditional_norm(fcv)
def gen(self, n_samples, n_reps=1, prior_mixin=0, verbose=None):
"""Generate from generator and z-transform
Parameters
----------
n_samples : int
Number of samples to generate
n_reps : int
Number of repeats per parameter
verbose : None or bool or str
If None is passed, will default to self.verbose
"""
assert n_reps == 1, 'n_reps > 1 is not yet supported'
verbose = self.verbose if verbose is None else verbose
n_pilot = np.minimum(n_samples, len(self.unused_pilot_samples[0]))
if n_pilot > 0 and self.generator.proposal is self.generator.prior: # reuse pilot samples
params = self.unused_pilot_samples[0][:n_pilot, :]
stats = self.unused_pilot_samples[1][:n_pilot, :]
self.unused_pilot_samples = \
(self.unused_pilot_samples[0][n_pilot:, :],
self.unused_pilot_samples[1][n_pilot:, :])
n_samples -= n_pilot
if n_samples > 0:
params_rem, stats_rem = self.generator.gen(n_samples,
prior_mixin=prior_mixin,
verbose=verbose)
params = np.concatenate((params, params_rem), axis=0)
stats = np.concatenate((stats, stats_rem), axis=0)
else:
params, stats = self.generator.gen(n_samples,
prior_mixin=prior_mixin,
verbose=verbose)
# z-transform params and stats
params = (params - self.params_mean) / self.params_std
stats = (stats - self.stats_mean) / self.stats_std
return params, stats
def reset(self, seed=None):
"""Resets inference method to a naive state, before it has seen any
real or simulated data. The following happens, in order:
1) The generator's proposal is set to None, and self.round is set to 0
2) The inference method is reseeded if a seed is provided
3) The network is reinitialized
4) Any additional resetting of state specific to each inference method
"""
self.generator.proposal = None
self.round = 0
if seed is not None:
self.reseed(seed)
self.reinit_network()
def reseed(self, seed):
"""reseed inference method's RNG, then generator, then network"""
self.rng.seed(seed=seed)
self.seed = seed
self.kwargs['seed'] = self.gen_newseed() # for consistent NN init
self.generator.reseed(self.gen_newseed()) # also reseeds prior + model
if isinstance(self.network, NeuralNet):
self.network.reseed(self.gen_newseed()) # for reproducible samples
# unfortunately, MAFs cannot currently be (re)seeded
def gen_newseed(self):
"""Generates a new random seed"""
if self.seed is None:
return None
else:
return self.rng.randint(0, 2**31)
def pilot_run(self, pilot_samples, n_stats, min_std=1e-4):
"""Pilot run in order to find parameters for z-scoring stats"""
if pilot_samples is None or \
(isint(pilot_samples) and pilot_samples == 0):
self.unused_pilot_samples = ([], [])
self.stats_mean = np.zeros(n_stats)
self.stats_std = np.ones(n_stats)
return
if isint(pilot_samples): # determine via pilot run
assert pilot_samples > 0
if self.seed is not None: # reseed generator for consistent inits
self.generator.reseed(self.gen_newseed())
verbose = '(pilot run) ' if self.verbose else False
params, stats = self.generator.gen(pilot_samples, verbose=verbose)
else: # samples were provided as an input
params, stats = pilot_samples
self.stats_mean = np.nanmean(stats, axis=0)
self.stats_std = np.nanstd(stats, axis=0)
assert not np.isnan(self.stats_mean).any(), "pilot run failed"
assert not np.isnan(self.stats_std).any(), "pilot run failed"
self.stats_std[self.stats_std == 0.0] = 1.0
self.stats_std = np.maximum(self.stats_std, min_std)
assert (self.stats_std > 0).all(), "pilot run failed"
ok_sims = np.logical_not(np.logical_or(np.isnan(stats).any(axis=1),
np.isnan(params).any(axis=1)))
self.unused_pilot_samples = (params[ok_sims, :], stats[ok_sims, :])
def predict(self, x, deterministic=True):
"""Predict posterior given x
Parameters
----------
x : array
Stats for which to compute the posterior
deterministic : bool
if True, mean weights are used for Bayesian network
"""
assert isinstance(self.network, NeuralNet)
# z-transform inputs
x_zt = (x - self.stats_mean) / self.stats_std
posterior = self.network.get_density(x_zt, deterministic=deterministic)
# z-transform outputs
if self.network.density == 'mog':
posterior = posterior.ztrans_inv(self.params_mean, self.params_std)
elif self.network.density == 'maf':
posterior.set_scale_and_offset(scale=self.params_std,
offset=self.params_mean)
else:
assert np.all(self.params_std == 1.0) and \
np.all(self.params_mean == 0.0)
return posterior
def compile_observables(self):
"""Creates observables dict"""
self.observables = {}
self.observables['loss.lprobs'] = self.network.lprobs
for p in self.network.aps:
self.observables[str(p)] = p
def monitor_dict_from_names(self, monitor=None):
"""Generate monitor dict from list of variable names"""
if monitor is not None:
observe = {}
if isinstance(monitor, str):
monitor = [monitor]
for m in monitor:
if m in self.observables:
observe[m] = self.observables[m]
else:
observe = None
return observe
